10 results on '"Toh, Kar-Ann"'
Search Results
2. Online Heterogeneous Face Recognition Based on Total-Error-Rate Minimization.
- Author
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Jang, Se-In, Tan, Geok-Choo, Toh, Kar-Ann, and Teoh, Andrew Beng Jin
- Subjects
HUMAN facial recognition software ,MACHINE learning ,ERROR functions ,IMAGE databases ,INFRARED spectra - Abstract
In this paper, we propose a recursive learning formulation for online heterogeneous face recognition (HFR). The main task is to compare between images which are acquired from different sensing spectrums for identity recognition. Using an extreme learning machine, the proposed recursive formulation seeks a direct optimization to the classification error goal where the solution converges exactly to the batch mode solution. Due to the nonlinear nature of the classification error objective function, formulation of a recursive solution that converges is an important and nontrivial task. Based on this recursive formulation, an online HFR system is designed. The system is evaluated using two challenging heterogeneous face databases with images captured under visible, near infrared and infrared spectrums. The proposed system shows promising performance which is comparable with that of competing state-of-the-arts. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
3. Stacking PCANet +: An Overly Simplified ConvNets Baseline for Face Recognition.
- Author
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Low, Cheng-Yaw, Teoh, Andrew Beng-Jin, and Toh, Kar-Ann
- Subjects
HUMAN facial recognition software ,PRINCIPAL components analysis ,ARTIFICIAL neural networks - Abstract
The principal component analysis network (PCANet) is asserted as a parsimonious stacking-based convolutional neural networks (CNNs) instance for generic object recognition including face. However, to be regarded a CNN resemblance, PCANet lacks a nonlinearity in between two successive convolutional layers. The multilayer PCANet (by neglecting the nonlinearity pre-requisite) is also deemed far-fetched for the network depth beyond two, due to feature dimensionality explosion. We thus devise a PCANet alternative, dubbed PCANet+ in this letter, to untangle these constraints. To be more precise, conforming to the CNN essentials, PCANet+ conveys a mean-pooling unit manipulating each feature map. On top of that, we streamline the PCANet topology to permit a deep construction with an expanded PCA filter ensemble. We scrutinize the PCANet+ performance using face recognition technology and other two faces in the wild datasets, namely, labeled faces in the wild and YouTube faces. The experimental results reveal that the PCANet+ descriptor prevails over its predecessor and other stacking-based descriptors in face identification and verification, serving a baseline for ConvNets. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
4. A Random Network Ensemble for Face Recognition.
- Author
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Choi, Kwontaeg, Toh, Kar-Ann, and Byun, Hyeran
- Abstract
In this paper, we propose a random network ensemble for face recognition problem, particularly for images with a large appearance variation and with a limited number of training set. In order to reduce the correlation within the network ensemble using a single type of feature extractor and classifier, localized random facial features have been constructed together with internally randomized networks. The ensemble classifier is finally constructed by combining these multiple networks via a sum rule. The proposed method is shown to have a better accuracy(31.5% and 15.3% improvements on AR and EYALEB databases respectively) and a better efficiency than that of the widely used PCA-SVM. [ABSTRACT FROM AUTHOR]
- Published
- 2009
- Full Text
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5. Incremental face recognition for large-scale social network services
- Author
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Choi, Kwontaeg, Toh, Kar-Ann, and Byun, Hyeran
- Subjects
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FACE perception , *ONLINE social networks , *WEB services , *PATTERN perception , *FEATURE extraction , *ARTIFICIAL neural networks - Abstract
Abstract: Due to the rapid growth of social network services such as Facebook and Twitter, incorporation of face recognition in these large-scale web services is attracting much attention in both academia and industry. The major problem in such applications is to deal efficiently with the growing number of samples as well as local appearance variations caused by diverse environments for the millions of users over time. In this paper, we focus on developing an incremental face recognition method for Twitter application. Particularly, a data-independent feature extraction method is proposed via binarization of a Gabor filter. Subsequently, the dimension of our Gabor representation is reduced considering various orientations at different grid positions. Finally, an incremental neural network is applied to learn the reduced Gabor features. We apply our method to a novel application which notifies new photograph uploading to related users without having their ID being identified. Our extensive experiments show that the proposed algorithm significantly outperforms several incremental face recognition methods with a dramatic reduction in computational speed. This shows the suitability of the proposed method for a large-scale web service with millions of users. [Copyright &y& Elsevier]
- Published
- 2012
- Full Text
- View/download PDF
6. Realtime training on mobile devices for face recognition applications
- Author
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Choi, Kwontaeg, Toh, Kar-Ann, and Byun, Hyeran
- Subjects
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FACE perception , *INFORMATION retrieval , *MOBILE apps , *RANDOM projection method , *ARTIFICIAL neural networks , *FEASIBILITY studies - Abstract
Abstract: Due to the increases in processing power and storage capacity of mobile devices over the years, an incorporation of realtime face recognition to mobile devices is no longer unattainable. However, the possibility of the realtime learning of a large number of samples within mobile devices must be established. In this paper, we attempt to establish this possibility by presenting a realtime training algorithm in mobile devices for face recognition related applications. This is differentiated from those traditional algorithms which focused on realtime classification. In order to solve the challenging realtime issue in mobile devices, we extract local face features using some local random bases and then a sequential neural network is trained incrementally with these features. We demonstrate the effectiveness of the proposed algorithm and the feasibility of its application in mobile devices through empirical experiments. Our results show that the proposed algorithm significantly outperforms several popular face recognition methods with a dramatic reduction in computational speed. Moreover, only the proposed method shows the ability to train additional samples incrementally in realtime without memory failure and accuracy degradation using a recent mobile phone model. [ABSTRACT FROM AUTHOR]
- Published
- 2011
- Full Text
- View/download PDF
7. Orthogonal filter banks with region Log-TiedRank covariance matrices for face recognition.
- Author
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Ng, Cong Jie, Low, Cheng Yaw, Toh, Kar-Ann, Kim, Jaihie, and Teoh, Andrew Beng Jin
- Subjects
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FILTER banks , *COVARIANCE matrices , *HUMAN facial recognition software , *HAAR transforms , *DISCRETE cosine transforms - Abstract
With the capability of fusing varying features from a specific image region, the Region Covariance Matrices (RCM) image descriptor has been evidenced plausible in face recognition. However, a systematic study for RCM, regarding which features to be fused in particular, remains absent. This paper therefore explores several features derived from the orthogonal filter ensembles, i.e., Identity Transform, Discrete Haar Transform, Discrete Cosine Transform, and Karhunen-Loève Transform, for feature encoding in RCM. Aside from that, we also outline a RCM variant, dubbed Region Log-TiedRank Covariance Matrices (RLTCM) in this paper. The RLTCM descriptor, on average, exhibits dramatic performance gain over RCM as well as state-of-the-art descriptors, especially when probe sets far deviated from the face gallery. Furthermore, we discern that the RLTCM descriptor defined based on Identity Transform, i.e., the simplest form of orthogonal filters, and other learning-free orthogonal filters yield impressive performance on par with the learning-based counterparts. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
8. Kernel Discriminant Embedding in face recognition
- Author
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Han, Pang Ying, Jin, Andrew Teoh Beng, and Toh Kar, Ann
- Subjects
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KERNEL functions , *DISCRIMINANT analysis , *FACE perception , *FEATURE extraction , *EMBEDDINGS (Mathematics) , *GRAPH theory , *NONLINEAR theories - Abstract
Abstract: In this paper, we present a novel and effective feature extraction technique for face recognition. The proposed technique incorporates a kernel trick with Graph Embedding and the Fisher’s criterion which we call it as Kernel Discriminant Embedding (KDE). The proposed technique projects the original face samples onto a low dimensional subspace such that the within-class face samples are minimized and the between-class face samples are maximized based on Fisher’s criterion. The implementation of kernel trick and Graph Embedding criterion on the proposed technique reveals the underlying structure of data. Our experimental results on face recognition using ORL, FRGC and FERET databases validate the effectiveness of KDE for face feature extraction. [Copyright &y& Elsevier]
- Published
- 2011
- Full Text
- View/download PDF
9. SVM-based feature extraction for face recognition
- Author
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Kim, Sang-Ki, Park, Youn Jung, Toh, Kar-Ann, and Lee, Sangyoun
- Subjects
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SUPPORT vector machines , *FEATURE extraction , *FACE perception , *DISCRIMINANT analysis , *LINEAR systems , *HYPERSURFACES , *DECISION making , *S-matrix theory - Abstract
Abstract: The primary goal of linear discriminant analysis (LDA) in face feature extraction is to find an effective subspace for identity discrimination. The introduction of kernel trick has extended the LDA to nonlinear decision hypersurface. However, there remained inherent limitations for the nonlinear LDA to deal with physical applications under complex environmental factors. These limitations include the use of a common covariance function among each class, and the limited dimensionality inherent to the definition of the between-class scatter. Since these problems are inherently caused by the definition of the Fisher''s criterion itself, they may not be solvable under the conventional LDA framework. This paper proposes to adopt a margin-based between-class scatter and a regularization process to resolve the issue. Essentially, we redesign the between-class scatter matrix based on the SVM margins to facilitate an effective and reliable feature extraction. This is followed by a regularization of the within-class scatter matrix. Extensive empirical experiments are performed to compare the proposed method with several other variants of the LDA method using the FERET, AR, and CMU-PIE databases. [Copyright &y& Elsevier]
- Published
- 2010
- Full Text
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10. A performance driven methodology for cancelable face templates generation
- Author
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Kim, Youngsung, Teoh, Andrew Beng Jin, and Toh, Kar-Ann
- Subjects
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PERFORMANCE evaluation , *BIOMETRIC identification , *EMPIRICAL research , *FACE perception , *FEATURE extraction , *PATTERN perception - Abstract
Abstract: In this paper, we propose a performance driven methodology for cancelable face templates generation. This is to address the issue of satisfying both the security and performance requirements at the same time. Essentially, the methodology consists of two transformations namely, an efficient feature extraction transformation and an error minimizing template transformation. The first transformation is achieved via a modified sparse random projection which extracts and transforms essential face features into cancelable templates. The second transformation is realized through a direct objective formulation to minimize the system''s total error rate. In order to facilitate convergence of the resulted minimization search, a modified sigmoid is proposed for an error counting step function approximation. Using two publicly available face databases, we empirically show an improved verification performance in terms of the equal error rate while hiding the face identity simultaneously. [Copyright &y& Elsevier]
- Published
- 2010
- Full Text
- View/download PDF
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