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1. Elliptical Local Binary Patterns for Face Recognition

Author

Huu-Tuan Nguyen, Alice Caplier, GIPSA - Architecture, Géométrie, Perception, Images, Gestes (GIPSA-AGPIG), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), and Caplier, Alice

Subjects
[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer science, Local binary patterns, business.industry, Dimensionality reduction, Gabor wavelet, 020207 software engineering, Pattern recognition, 02 engineering and technology, Ellipse, Facial recognition system, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Face (geometry), Principal component analysis, 0202 electrical engineering, electronic engineering, information engineering, Feature (machine learning), 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; In this paper, we propose a novel variant of Local Binary Patterns (LBP) so-called Elliptical Local Binary Patterns (ELBP) for face recognition. In ELBP, we use horizontal and vertical ellipse patterns to capture micro facial feature of face images in both horizontal and vertical directions. ELBP is applied in face recognition with dimension reduction step by Whitened Principal Component Analysis (WPCA). Our experiment results upon AR, FERET and Surveillance Cameras Face (SCface) databases prove the advantages of ELBP over LBP for face recognition under different conditions and with ELBP WPCA we can get very remarkable results.

Published
2013
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2. How far we can improve micro features based face recognition systems?

Author

Alice Caplier, Ngoc-Son Vu, Huu-Tuan Nguyen, GIPSA - Architecture, Géométrie, Perception, Images, Gestes (GIPSA-AGPIG), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Vesalis (Vesalis), PME, and Caplier, Alice

Subjects
Pixel, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, business.industry, Local binary patterns, Dimensionality reduction, Feature extraction, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, 02 engineering and technology, Thresholding, Facial recognition system, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 020204 information systems, Face (geometry), Singular value decomposition, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Mathematics
Abstract

This paper presents improvements for face recognition methods that use LBP descriptor as a main technique in encoding micro features of face images. Our improvements are focused on the feature extraction and dimension reduction steps. In feature extraction, we use a variant of Local Binary Pattern (LBP) so-called Elliptical Local Binary Pattern (ELBP), which is more efficient than LBP for extracting micro facial features of the human face. ELBP of one pixel is built by thresholding its gray value with its P neighboring pixels on a horizontal ellipse. ELBP operator is applied in Pattern of Oriented Edge Magnitudes (POEM) to build Elliptical POEM (EPOEM) descriptor. The dimension reduction step is conducted by using Singular Value Decomposition (SVD) based Whitened Principal Component Analysis (WPCA). For performance evaluation of our improvements, we compare them with LBP based, POEM based approaches and other popular face recognition systems. The experimental results on state-of-the-art FERET and AR face databases prove the advantages and effectiveness of our improvements.

Published
2012
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3. Multiple patterns of gradient magnitudes for face recognition

Author

Alice Caplier, Ngoc-Son Vu, Huu-Tuan Nguyen, GIPSA - Architecture, Géométrie, Perception, Images, Gestes (GIPSA-AGPIG), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Vesalis (Vesalis), PME, and Caplier, Alice

Subjects
[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, business.industry, Computer science, Pattern recognition, 02 engineering and technology, 01 natural sciences, Facial recognition system, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Robustness (computer science), 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, 010306 general physics, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Describing efficiently faces is a task of mounting importance. Most of existing algorithms do not address all the three criteria, i.e., robustness, distinctiveness and low computational cost. Inspired by recent features so-called POEM (Patterns of Oriented Edge Magnitudes) which is argued balancing well the three concerns, we first provide an improvement to it and then propose novel features so-called self-POEM considering the relations between edge distributions along different directions of one region itself. Self-POEM provides the complementary strength which is not present in POEM. Combing them together, a more robust algorithm is obtained and by experiments we prove that our approach is more efficient than contemporary ones.

Published
2012
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4. Mining patterns of orientations and magnitudes for face recognition

Author

Alice Caplier, Ngoc-Son Vu, GIPSA - Architecture, Géométrie, Perception, Images, Gestes (GIPSA-AGPIG), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), and Caplier, Alice

Subjects
021110 strategic, defence & security studies, business.industry, Computer science, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 0211 other engineering and technologies, Pattern recognition, 02 engineering and technology, Facial recognition system, Edge detection, Image (mathematics), Discriminative model, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Face (geometry), Principal component analysis, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, Enhanced Data Rates for GSM Evolution, business, Representation (mathematics), [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Good face recognition system is one which quickly delivers high accurate results to the end user. For this purpose, face representation must be robust, discriminative and also of low computational cost in both terms of time and space. Inspired by recently proposed feature set so-called POEM (Patterns of Oriented Edge Magnitudes) which considers the relationships between edge distributions of different image patches and is argued balancing well the three concerns, this work proposes to further exploit patterns of both orientations and magnitudes for building more efficient algorithm. We first present novel features called Patterns of Dominant Orientations (PDO) which consider the relationships between "dominant" orientations of local image regions at different scales. We also propose to apply the whitened PCA technique upon both the POEM and PDO based representations to get more compact and discriminative face descriptors. We then show that the two methods have complementary strength and that by combining the two descriptors, one obtains stronger results than either of them considered separately. By experiments carried out on several common benchmarks, including both frontal and nonfrontal FERET as well as the AR datasets, we prove that our approach is more efficient than contemporary ones.

Published
2011

5. Biologically Inspired Processing for Lighting Robust Face Recognition

Author

Ngoc-Son Vu, Alice Caplier, GIPSA - Architecture, Géométrie, Perception, Images, Gestes (GIPSA-AGPIG), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Vesalis (Vesalis), PME, Jucheng Yang and Loris Nanni (Eds.), and Caplier, Alice

Subjects
Biometrics, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, business.industry, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Normalization (image processing), Pattern recognition, Access control, 02 engineering and technology, Linear discriminant analysis, 01 natural sciences, Facial recognition system, 010309 optics, Support vector machine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Eigenface, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, business, Hidden Markov model, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

Due to its wide variety of real-life applications, ranging from user-authentication (access control, ATM) to video surveillance and law enforcements, face recognition has been one of the most active research topics in computer vision and pattern recognition. Also, it has obvious advantages over other biometric techniques, since it is natural, socially well accepted, and notably non-intrusive. In reality, several reliable biometrics authentication techniques are available and widely used nowadays (such as iris or fingerprint), but they mostly rely on an active participation of the user. On the contrary, facial biometric demands very little cooperation from the user; thanks to this user-friendly capability, face recognition is said to be non-intrusive. Over the last decades, significant progress has been achieved in face recognition area. Since the seminal work of Turk and Pentland (Turk & Pentland, 1991), where the Principal Component Analysis (PCA) is proposed to apply to face images (Eigenfaces), more sophisticated techniques for face recognition appear, such as Fisherfaces (Belhumeur et al., 1997), based on linear discriminant analysis (LDA), Elastic Bunch Graph Matching (EBGM) (Wiskott et al., 1997), as well as approaches based upon Support Vector Machines (SVM) (Phillips, 1999), or Hidden Markov Models (HMM) (Nefian & III, 1998; Vu & Caplier, 2010b), etc. Nevertheless, face recognition, notably under uncontrolled scenarios, remains active and unsolved. Among many factors affecting the performance of face recognition systems, illumination is known to be one of the most significant. Indeed, it was proven, both theoretically (Moses et al., 1994) and experimentally (Adini et al., 1997) that image variation due to lighting changes is more significant than that due to different personal identities. In other words, the difference between two face images of the same individual taken under varying lighting conditions is larger than the difference between any two face images taken under the same lighting conditions, as illustrated in Fig. 1. Inspired by the great ability of human retina that enables the eyes to see objects in different lighting conditions, we present in this chapter a novel method of illumination normalization by simulating the performance of its two layers: the photoreceptors and the outer plexiform layer. Thus, we say the algorithm biologically inspired. The rest of the chapter is structured as follows: Section 2 briefly discusses the related work; Section 3 presents the model of retinal processing and its advantage. In Section 4, the 6

Published
2011

6. Face recognition with patterns of oriented edge magnitudes

Author

Ngoc-Son Vu, Alice Caplier, Vesalis (Vesalis), PME, GIPSA - Architecture, Géométrie, Perception, Images, Gestes (GIPSA-AGPIG), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), and Caplier, Alice

Subjects
Pixel, Local binary patterns, business.industry, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Gabor wavelet, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 020206 networking & telecommunications, Pattern recognition, 02 engineering and technology, Facial recognition system, Discriminative model, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Robustness (computer science), Feature (computer vision), Histogram, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Mathematics, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; This paper addresses the question of computationally inexpensive yet discriminative and robust feature sets for real-world face recognition. The proposed descriptor named Patterns of Oriented Edge Magnitudes (POEM) has desirable properties: POEM (1) is an oriented, spatial multi-resolution descriptor capturing rich information about the original image; (2) is a multi-scale self-similarity based structure that results in robustness to exterior variations; and (3) is of low complexity and is therefore practical for real-time applications. Briefly speaking, for every pixel, the POEM feature is built by applying a self-similarity based structure on oriented magnitudes, calculated by accumulating a local histogram of gradient orientations over all pixels of image cells, centered on the considered pixel. The robustness and discriminative power of the POEM descriptor is evaluated for face recognition on both constrained (FERET) and unconstrained (LFW) datasets. Experimental results show that our algorithm achieves better performance than the state-of-the-art representations. More impressively, the computational cost of extracting the POEM descriptor is so low that it runs around 20 times faster than just the first step of the methods based upon Gabor filters. Moreover, its data storage requirements are 13 and 27 times smaller than those of the LGBP (Local Gabor Binary Patterns) and HGPP (Histogram of Gabor Phase Patterns) descriptors respectively.

Published
2010

7. Patch-based similarity HMMs for face recognition with a single reference image

Author

Alice Caplier, Ngoc-Son Vu, Vesalis (Vesalis), PME, GIPSA - Architecture, Géométrie, Perception, Images, Gestes (GIPSA-AGPIG), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), and Caplier, Alice

Subjects
FERET database, Similarity (geometry), Computer science, business.industry, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Speech recognition, Process (computing), 020207 software engineering, Pattern recognition, 02 engineering and technology, Facial recognition system, Spatial relation, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Face (geometry), 0202 electrical engineering, electronic engineering, information engineering, Maximum a posteriori estimation, 020201 artificial intelligence & image processing, Artificial intelligence, Hidden Markov model, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; In this paper we present a new architecture for face recognition with a single reference image, which separates the training process from the recognition process completely. In the training stage, by using a database containing various individuals, the spatial relations between face components are represented by two Hidden Markov Models, one of which models the similarity between face images coming from the same individual (within-subject similarities), and the other modelling inter-subject differences. This allows us to take a pair of face images, neither of which has been seen before, and determine whether or not they come from the same individual. Whilst other face-recognition HMMs use Maximum Likelihood, we have tested our approach using both Maximum Likelihood and MAP estimation, and find that MAP estimation provides better results. Importantly, the training database can be entirely separated from the gallery and test images: this means that adding new individuals to the system can be done without re-training. We present results based upon models trained on the FERET training dataset, and demonstrate that these give excellent recognition rates on both the FERET database itself and more impressively the unseen AR database. When compared to other HMM based face recognition techniques, our algorithm is of much lower complexity due to the small size of our observation sequence.

Published
2010

8. Comparison between EOG and high frame rate camera for drowsiness detection

Author

Alice Caplier, Antoine Picot, Sylvie Charbonnier, SA-IGA (GIPSA-SA-IGA), Département Automatique (GIPSA-DA), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Département Images et Signal (GIPSA-DIS), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), GPIG (GIPSA-GPIG), Département Images et Signal (GIPSA-DIS), IEEE, GIPSA - Signal et Automatique pour le Diagnostic et la Surveillance (GIPSA-SA-IGA), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Département Automatique (GIPSA-DA), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), GIPSA - Géométrie, Perception, Images, Geste (GIPSA-GPIG), and Caplier, Alice

Subjects
[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer science, Feature extraction, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, 01 natural sciences, Signal, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, medicine, Computer vision, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Pixel, medicine.diagnostic_test, business.industry, 010401 analytical chemistry, Electrooculography, Frame rate, 0104 chemical sciences, Face (geometry), 020201 artificial intelligence & image processing, Artificial intelligence, High frame rate, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Energy (signal processing)
Abstract

International audience; Drowsiness is responsible for a large number car crashes. Blinks analysis from electrooculogram (EOG) signal brings reliable information on drowsiness but EOG recording condition can be really disturbing for the driver. On the other hand, video approaches seem a lot more practical but the standard acquisition rate does not give the same accuracy than EOG for blinks analysis. So, a high frame rate camera seems a good compromise. The purpose of this paper is to study to what extent a high speed camera could replace the EOG for the extraction of blinks features in order to design a system to detect drowsiness. An original method to detect and characterize blinks from the video is presented. This method uses two energy signals extracted from the video analysis: one related to the contours of the eyes and the other one to the moving contours. A comparison between the different features extracted from the EOG and from the video is then performed. This study shows that duration, frequency, PERCLOS 80 and dynamic features extracted from the EOG and from the video signals are highly correlated. The frame rate influence on the accuracy of the different features extracted is also studied

Published
2009

9. Inner and outer lip contour tracking using cubic curve parametric models

Author

Sébastien Stillittano, Alice Caplier, Vincent Girondel, Caplier, Alice, IEEE, Vesalis (Vesalis), PME, GIPSA - Géométrie, Perception, Images, Geste (GIPSA-GPIG), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)

Subjects
030506 rehabilitation, genetic structures, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Feature extraction, 02 engineering and technology, Luminance, Cubic plane curve, Edge detection, 03 medical and health sciences, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, stomatognathic system, 0202 electrical engineering, electronic engineering, information engineering, Computer vision, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Parametric statistics, Mathematics, business.industry, Tracking, Pattern recognition, Image segmentation, Image edge analysis, stomatognathic diseases, Parametric model, Chrominance, 020201 artificial intelligence & image processing, Artificial intelligence, 0305 other medical science, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; The first step in lipreading applications is mouth contour extraction to provide the link between lip shape and the oral message. In our approach, the lip contours are detected in the first image with the two algorithms developed in [1] and [2] for static images. On subsequent images of the sequence, several key points (mouth corners and inner and outer middle contour points) are tracked with the Lucas-Kanade method to define an initial parametric lip model of the mouth. According to a combined luminance and chrominance gradient, the model is optimized and precisely locked onto the lip contours. The algorithm performances are evaluated with regard to a lipreading application.

Published
2009
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10. Efficient statistical face recognition across pose using Local Binary Patterns and Gabor wavelets

Author

Ngoc-Son Vu, Alice Caplier, Vesalis (Vesalis), PME, GIPSA - Géométrie, Perception, Images, Geste (GIPSA-GPIG), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), OSEO, Biorafale, and Caplier, Alice

Subjects
[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer science, Local binary patterns, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, Facial recognition system, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Robustness (computer science), 0202 electrical engineering, electronic engineering, information engineering, Three-dimensional face recognition, Computer vision, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Pixel, business.industry, Gabor wavelet, Wavelet transform, Pattern recognition, ComputingMethodologies_PATTERNRECOGNITION, 020201 artificial intelligence & image processing, Artificial intelligence, business, Combination method, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; The performance of face recognition systems can be dramatically degraded when the pose of the probe face is different from the gallery face. In this paper, we present a pose robust face recognition model, centered on modeling how face patches change in appearance as the viewpoint varies. We present a novel model based on two robust local appearance descriptors, Gabor wavelets and Local Binary Patterns (LBP). These two descriptors have been widely exploited for face recognition and different strategies for combining them have been investigated. However, to the best of our knowledge, all existing combination methods are designed for frontal face recognition. We introduce a local statistical framework for face recognition across pose variations, given only one frontal reference image. The method is evaluated on the Feret pose dataset and experimental results show that we achieve very high recognition rates over the wide range of pose variations presented in this challenging dataset.

Published
2009
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11. Illumination-robust face recognition using retina modeling

Author

Ngoc-Son Vu, Alice Caplier, Caplier, Alice, IEEE, Vesalis (Vesalis), PME, GIPSA - Géométrie, Perception, Images, Geste (GIPSA-GPIG), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)

Subjects
Normalization (statistics), Difference of Gaussians, Computer science, Local binary patterns, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Feature extraction, Normalization (image processing), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, Facial recognition system, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, medicine, Three-dimensional face recognition, Computer vision, retinal processing, Face recognition, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Retina, Pixel, business.industry, illumination invariant, 020206 networking & telecommunications, Pattern recognition, medicine.anatomical_structure, 020201 artificial intelligence & image processing, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Illumination variations that occur on face images degrade the performances of face recognition systems. In this paper, we propose a novel method of illumination normalization based on retina modeling by combining two adaptive nonlinear functions and a Difference of Gaussians filter. The proposed algorithm is evaluated on the Yale B database, the Feret illumination database by using two face recognition methods: PCA and Local Binary Pattern (LBP). Experimental results show that the proposed method achieves very high recognition rates even for the most challenging illumination conditions. Our algorithm has also a low computational complexity

Published
2009

12. Hypovigilence Analysis: Open or Closed Eye or Mouth ? Blinking or Yawning Frequency

Author

Alexandre Benoit, Alice Caplier, Laboratoire des images et des signaux (LIS), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Université Joseph Fourier - Grenoble 1 (UJF), and Caplier, Alice

Subjects
Motion analysis, genetic structures, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, business.industry, Computer science, Motion detection, Context (language use), 02 engineering and technology, Filter (signal processing), Object detection, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Feature (computer vision), Motion estimation, Human visual system model, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 030217 neurology & neurosurgery, ComputingMilieux_MISCELLANEOUS, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

This paper proposes a frequency method to estimate the state open or closed of eye and mouth and to detect associated motion events such as blinking and yawning. The context of that work is the detection of hypovigilence state of a user such as a driver, a pilot. In A. Benoit and Caplier (2005) we proposed a method for motion detection and estimation which is based on the processing achieved by the human visual system. The motion analysis algorithm the filtering step occurring at the retina level and the analysis done at the visual cortex level. This method is used to estimate the motion of eye and mouth: blinking is related to fast vertical motion of the eyelid and yawning is related to large vertical mouth opening. The detection of the open or closed state of the feature is based on the analysis of the total energy of the image at the output of the retina filter: this energy is higher for open features. The absolute level of energy associated to a specific state being different from a person to another and for different illumination conditions, the energy level associated to each state open or closed is adaptive and is updated each time a motion event (blinking or yawning) is detected. No constraint about motion is required. The system is working in real time and under all type of lighting conditions since the retina filtering is able to cope with illumination variations. This allows to estimate blinking and yawning frequencies which are clues of hypovigilance.

13. A Human Body Analysis System

Author

Laurent Bonnaud, Alice Caplier, Vincent Girondel, Caplier, Alice, Laboratoire des images et des signaux (LIS), and Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Université Joseph Fourier - Grenoble 1 (UJF)

Subjects
Computer science, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, lcsh:TK7800-8360, 02 engineering and technology, Metaverse, lcsh:Telecommunication, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Home automation, lcsh:TK5101-6720, 0202 electrical engineering, electronic engineering, information engineering, Computer vision, Segmentation, Electrical and Electronic Engineering, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Ambient intelligence, business.industry, lcsh:Electronics, 020206 networking & telecommunications, Hardware and Architecture, Signal Processing, 020201 artificial intelligence & image processing, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Lying, Classifier (UML)
Abstract

International audience; This paper describes a system for human body analysis (segmentation, tracking, face/hands localisation, posture recognition) from a single view that is fast and completely automatic. The system first extracts low-level data and uses part of the data for high-level interpretation. It can detect and track several persons even if they merge or are completely occluded by another person from the camera's point of view. For the high-level interpretation step, static posture recognition is performed using a belief theory-based classifier. The belief theory is considered here as a new approach for performing posture recognition and classification using imprecise and/or conflicting data. Four different static postures are considered: standing, sitting, squatting, and lying. The aim of this paper is to give a global view and an evaluation of the performances of the entire system and to describe in detail each of its processing steps, whereas our previous publications focused on a single part of the system. The efficiency and the limits of the system have been highlighted on a database of more than fifty video sequences where a dozen different individuals appear. This system allows real-time processing and aims at monitoring elderly people in video surveillance applications or at the mixing of real and virtual worlds in ambient intelligence systems.

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