177 results on '"Yau, Wei-Yun"'
Search Results
152. Singular Points Detection Using Interactive Mechanism In Fingerprint Images
- Author
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Li, Jun, primary, Yau, Wei-Yun, additional, and Wang, Han, additional
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- 2006
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153. Model-guided deformable hand shape recognition without positioning aids
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Xiong, Wei, primary, Toh, Kar-Ann, additional, Yau, Wei-Yun, additional, and Jiang, Xudong, additional
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- 2005
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154. Fast face detection and localization from multi-views using statistical approach.
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Anvar, Seyed Mohammad Hassan, Yau, Wei-Yun, and Teoh, Eam Khwang
- Abstract
Window-based face detection methods are fast. However their results are coarse, pose dependent and require fine face alignment for face analysis. Recently a statistical approach is introduced by Toews and Arbel [1], which is able to detect faces in multiple poses and does not require face alignment. However, their method is slow compared to the window-based method. In this paper, we proposed a method, which capable of detecting faces in multiple poses in near real time and also does not require face alignment. Experimental results show that our proposed method has comparable accuracy with the Toews and Arbel's method but has significantly lower processing time. [ABSTRACT FROM PUBLISHER]
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- 2011
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155. Multimedia applications for standard IPTV: ITU-T IPTV standard suite in action.
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Kawamori, Masahito, Moreno, Marcelo Fereira, Matsubara, Fernando M., and Yau, Wei-Yun
- Published
- 2011
156. Enhancing IPTV personalization.
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Yau, Wei-Yun, Wang, Jiangang, Hou, Zujun, and Lai, Fon-Lin
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- 2010
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157. Speaker gender recognition using score level fusion by AdaBoost.
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Ichino, M., Komatsu, N., Wang Jian-Gang, and Yau Wei Yun
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- 2010
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158. A Video-Based Drowning Detection System.
- Author
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Kam, Alvin H., Lu, Wenmiao, and Yau, Wei-Yun
- Abstract
This paper provides new insights into robust human tracking and semantic event detection within the context of a novel real-time video surveillance system capable of automatically detecting drowning incidents in a swimming pool. An effective background model that incorporates prior knowledge about swimming pools and aquatic environments enables swimmers to be reliably detected and tracked despite the significant presence of water ripples, splashes and shadows. Visual indicators of water crises are identified based on professional knowledge of water crisis recognition and modelled by a hierarchical set of carefully chosen swimmer descriptors. An effective alarm generation methodology is then developed to enable the timely detection of genuine water crises while minimizing the number of false alarms. The system has been tested on numerous instances of simulated water crises and potential false alarm scenarios with encouraging results. [ABSTRACT FROM AUTHOR]
- Published
- 2006
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159. On-Chip Genetic Algorithm Optimized Pulse Based RBF Neural Network for Unsupervised Clustering Problem.
- Author
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Jiao, Licheng, Wang, Lipo, Gao, Xinbo, Liu, Jing, Wu, Feng, Low, Kay-Soon, Krishnan, Vinitha, Zhuang, Hualiang, and Yau, Wei-Yun
- Abstract
This paper presents a new technique for solving the unsupervised clustering problem using a pulse based RBF neural network that is optimized by a genetic algorithm. In this new approach, the neuron encodes the information using the firing times of pulses that are generated by the neurons. Due to hardware's speed advantage and its ability to parallelize along with reprogramability and reconfigurability, the field programmable gate array (FPGA) is used for implementing the proposed approach on-chip. The developed on-chip system is capable of solving various problems requiring large data set in real time. Experimental verification using a sample data set and Fisher's Iris data set has shown the feasibility of the developed system. [ABSTRACT FROM AUTHOR]
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- 2006
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160. A Bimodal Palmprint Verification System.
- Author
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Zhang, David, Jain, Anil K., Tan, Tai-Kia, Ng, Cheng-Leong, Toh, Kar-Ann, Eng, How-Lung, Yau, Wei-Yun, and Srinivasan, Dipti
- Abstract
Hand-based biometrics such as fingerprint and palmprint had been widely accepted because of their convenience and ease in usage without intruding much to one's privacy such as face. The aim of this work is to develop a new point-based algorithm for palmprint feature extraction and perform reliable verification based on the extracted features. This point-based recognition system is next used as part of a bimodal palmprint recognition system combining with a DCT-based (Discrete Cosine Transform) algorithm for identity verification. The performance of the integrated system is evaluated using physical palmprint images. Keywords: Biometrics, Palmprint Recognition, Multimodal Biometrics, and Identity Verification. [ABSTRACT FROM AUTHOR]
- Published
- 2005
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161. Identity Verification Through Palm Vein and Crease Texture.
- Author
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Zhang, David, Jain, Anil K., Toh, Kar-Ann, Eng, How-Lung, Choo, Yuen-Siong, Cha, Yoon-Leon, Yau, Wei-Yun, and Low, Kay-Soon
- Abstract
In this paper, an identity verification framework which combines pattern information from the palm-vein and the palm-crease texture is proposed. Main feature of this system is the use of a low cost Near-Infra-Red (NIR) camera instead of the more expensive infra-red thermal camera for palm image capture. Our preliminary experiments show that useful information from palm-vein and palm-crease texture can be effectively extracted for identity verification using a simple setup to contain the camera. Keywords:Biometrics, Multimodal Biometrics, Palm-vein Recognition, Palm-print Recognition and Pattern Classification. [ABSTRACT FROM AUTHOR]
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- 2005
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162. Multiview Face Detection and Registration Requiring Minimal Manual Intervention.
- Author
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Anvar, Seyed Mohammad Hassan, Yau, Wei-Yun, and Teoh, Eam Khwang
- Subjects
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MATHEMATICAL regularization , *VIDEO surveillance , *TEMPLATE matching (Digital image processing) , *LIGHTING , *IMAGE processing , *PIXELS - Abstract
Most face recognition systems require faces to be detected and localized a priori. In this paper, an approach to simultaneously detect and localize multiple faces having arbitrary views and different scales is proposed. The main contribution of this paper is the introduction of a face constellation, which enables multiview face detection and localization. In contrast to other multiview approaches that require many manually labeled images for training, the proposed face constellation requires only a single reference image of a face containing two manually indicated reference points for initialization. Subsequent training face images from arbitrary views are automatically added to the constellation (registered to the reference image) based on finding the correspondences between distinctive local features. Thus, the key advantage of the proposed scheme is the minimal manual intervention required to train the face constellation. We also propose an approach to identify distinctive correspondence points between pairs of face images in the presence of a large amount of false matches. To detect and localize multiple faces with arbitrary views, we then propose a probabilistic classifier-based formulation to evaluate whether a local feature cluster corresponds to a face. Experimental results conducted on the FERET, CMU, and FDDB datasets show that our proposed approach has better performance compared to the state-of-the-art approaches for detecting faces with arbitrary pose. [ABSTRACT FROM AUTHOR]
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- 2013
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163. Fast match-on-card technique using in-matcher clustering with ISO minutia template
- Author
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Chen, Tai-Pang, Yau, Wei-Yun, and Jiang, Xudong
- Abstract
Fingerprint match-on-card is receiving more attention from society because of higher level of security and less privacy concern. The ISO/IEC19794-2 for match-on-card defines the minutiae data format that contains the basic information. Therefore, match-on-card is a challenge, especially when dealing with deformation and common correspondence for alignment. In this paper, a novel in-matcher clustering method is proposed to search for the matched minutia clusters to cater for deformation. Moreover, a further matching step using Mahalanobis distance to measure the inter-cluster similarity is proposed to remove the wrongly matched clusters. Finally, the overall match score is generated by combining the scores from matched clusters and the geometrical structure of clusters. The proposed algorithm achieved an average EER ⇐ 5.1979% using all FVC databases. In the NIST evaluation, the achieved false match rate (FMR) = 0.001 and false non-match rate (FNMR) = 0.08 and the average on-card verification time is 1.01s using 8-bit smartcard.
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- 2015
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164. Active Learning for Solving the Incomplete Data Problem in Facial Age Classification by the Furthest Nearest-Neighbor Criterion.
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Wang, Jian-Gang, Sung, Eric, and Yau, Wei-Yun
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HUMAN facial recognition software ,LEARNING classifier systems ,AGE groups ,ESTIMATION theory ,SUPPORT vector machines ,MACHINE learning ,DATABASES ,ALGORITHMS - Abstract
Facial age classification is an approach to classify face images into one of several predefined age groups. One of the difficulties in applying learning techniques to the age classification problem is the large amount of labeled training data required. Acquiring such training data is very costly in terms of age progress, privacy, human time, and effort. Although unlabeled face images can be obtained easily, it would be expensive to manually label them on a large scale and getting the ground truth. The frugal selection of the unlabeled data for labeling to quickly reach high classification performance with minimal labeling efforts is a challenging problem. In this paper, we present an active learning approach based on an online incremental bilateral two-dimension linear discriminant analysis (IB2DLDA) which initially learns from a small pool of labeled data and then iteratively selects the most informative samples from the unlabeled set to increasingly improve the classifier. Specifically, we propose a novel data selection criterion called the furthest nearest-neighbor (FNN) that generalizes the margin-based uncertainty to the multiclass case and which is easy to compute, so that the proposed active learning algorithm can handle a large number of classes and large data sizes efficiently. Empirical experiments on FG-NET and Morph databases together with a large unlabeled data set for age categorization problems show that the proposed approach can achieve results comparable or even outperform a conventionally trained active classifier that requires much more labeling effort. Our IB2DLDA-FNN algorithm can achieve similar results much faster than random selection and with fewer samples for age categorization. It also can achieve comparable results with active SVM but is much faster than active SVM in terms of training because kernel methods are not needed. The results on the face recognition database and palmprint/palm vein database showed that our approach can handle problems with large number of classes. Our contributions in this paper are twofold. First, we proposed the IB2DLDA-FNN, the FNN being our novel idea, as a generic on-line or active learning paradigm. Second, we showed that it can be another viable tool for active learning of facial age range classification. [ABSTRACT FROM AUTHOR]
- Published
- 2011
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165. A review on fingerprint orientation estimation.
- Author
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Hou, Zujun, Yau, Wei-Yun, and Wang, Yue
- Abstract
Fingerprint orientation plays important roles in fingerprint enhancement, fingerprint classification, and fingerprint recognition. This paper critically reviews the primary advances on fingerprint orientation estimation. Advantages and limitations of existing methods have been addressed. Issues on future development have been discussed. Copyright © 2010 John Wiley & Sons, Ltd. The paper reviews the advances on fingerprint orientation estimation and discusses the issues on future development. [ABSTRACT FROM AUTHOR]
- Published
- 2011
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166. Robust hand-eye coordination
- Author
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Yau, Wei-Yun and WANG, HAN
- Abstract
Industrial cameras are coupled into robotic systems to increase the flexibility of the robots. Such hand-eye coordination usually requires calibration of the cameras to compute the three-dimensional (3D) coordinates of positions in the robot space. This paper describes a new hand-eye coordination approach which does not require camera calibration. Instead, we propose the use of relative stereo disparity to compute the relative depth between the perceived objects. Incorporating the relative depth measure into the image space converts the hand-eye coordination problem into a linear transformation between the pseudo-3D image space and the 3D robot space. Moreover, the transformation matrix involved is square and can be easily estimated and updated by using visual feedback. The proposed method is fast and simple, making it feasible for real-time visual feedback implementation. Furthermore, since no calibration is required, the method proposed is robust to substantial changes in the hand-eye system configuration. Experiments are conducted to verify the accuracy and robustness of the proposed method. The main contributions of this paper are: (i) introducing a stereo attribute that measures relative depth, (ii) formulating a pseudo-3D image space, (iii) relating the pseudo-3D image space to the robot space for computing the hand-eye transformation and (iv) realizing a robust hand-eye coordination system which incorporates visual feedback control.
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- 1996
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167. Adaptive enhancement of CT images.
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Yau Wei Yun, Myint, T., and Jia Jian Cheng
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- 1993
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168. Prediction of fingerprint orientation.
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Li Jun and Yau Wei Yun
- Published
- 2004
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169. Automated recognition of highly complex human behavior.
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Kam, A.H., Toh Kar Ann, Eng How Lung, Yau Wei Yun, and Wang Junxian
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- 2004
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170. Automated analysis of human family relationship using facial features
- Author
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Mohammad Ghahramani., Yau Wei-Yun, Teoh Eam Khwang, and School of Electrical and Electronic Engineering
- Subjects
Family relationship ,Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision [DRNTU] ,Computer science ,business.industry ,Artificial intelligence ,business - Abstract
There has been increasing interest on the use of video surveillance to analyze human biometrics, especially in places where the chance of occurring crime is high, such as in large cities. As an example, 2185 children are being reported missing daily in the United States. It has been rarely looked into belonging a person to the same family as a soft biometric modal. We term the ability to verify query images as family members “family (kinship) verification.” Several studies have been conducted on family photo albuming, but the problem of family verification has not yet been directly addressed and analyzed. The lack of comprehensive published family datasets is evidence that the matter has received very little attention to date. This thesis focuses on providing solutions for challenges of family verification as a recent application of computer vision. We propose frameworks to recognize members of the same family based on the facts and psychological studies on family members’ resemblance to each other. We also investigate the demanding case of missing/unknown member verification as a critical real-life application that will benefit from this research. In the first major section of this thesis, we study true assumptions behind the resemblance of family members to one another, and the challenges of family verification. We develop traditional face recognition approaches to facilitate family verification by extraction of facial resemblance among family members. We investigate the very recent family verification problem, define scenarios and collect a dataset of family albums. Our aim is to recognize if the query sample belongs to the same family. In the first stage, we investigate facial resemblance extraction among family members to perform family verification. Similarities of facial patches are utilized as multiple experts to recognize family members. Finally, our proposed inter-patch constraint-free algorithm of facial patches analysis determines the redundant patches among family members. On average, five facial patches (29% of the facial patches) are chosen to achieve the same accuracy obtained by using all patches for family verification. Consequently, the computational time is saved by 71% compared to utilizing all patches. The second major section of our thesis is focused on family verification in real-time systems to obtain more discriminative features with the least dimension. Firstly, we minimize the quantization error of the Local Binary Pattern (LBP) operator through the incorporation of uniformly-sampled thresholds for LBP (UTLBP). Up to 3.4% improvement was achieved for family verification compared to various features in the literature, while the feature dimension is considerably smaller than the well-known Haar-like features (60%). Moreover, the proposed operator is more robust than the conventional operators, as tested on the large CAS-PEAL dataset against background, illumination, aging, and accessories changes. It achieves up to 8% performance improvement depending on the CAS-PEAL probe set, compared to the best operator in the literature. Then, we propose a novel redundant feature removal algorithm based on the fact that there is less similarity among the faces of family members than that of an individual’s face samples. The proposed algorithms are then employed on the UTLBP feature operator to select the most informative set of thresholds to outperform the Haar-like features, with 20 times less feature dimension. The proposed approach converges 10 times faster than the state-of-the-art face recognition algorithms do. The third contribution of the thesis is to utilize from each member’s image segment resemblance to the entire family. Our proposed method is to incorporate the degree of resemblance of each individual member’s patch to perform family verification. Our analysis and the important consensuses of psychologists’ studies reveal that the facial resemblance between family members differs from member to member and is facial patch specific. We propose to estimate the amount of resemblance for each patch based on the prior information of the member. As the score level fusion is preferred in the literature due to its wealth of information and low data dimension, we embed the available a priori information in the score fusion rule. The proposed method outperforms the state-of-the-art score fusion rules in all scenarios by 14%. Moreover, the resemblance estimation slightly outperforms human observation in the designated survey. The proposed method could also improve object family verification on the selected Caltech-256 database by 8%. In summary, two automated frameworks for recognizing family members are proposed. The first framework utilizes UTLBP operator and the novel redundant feature removal provides fast and low computational algorithms suitable for real-time processing. The other framework of facial patch resemblance extraction among family members benefits from offline training with parallel computing capability. Finally, the enhancement of family verification can potentially solve critical applications of daily life to find missing children and unknown parents. DOCTOR OF PHILOSOPHY (EEE)
- Published
- 2019
171. Fingerprint processing based on phase portrait model
- Author
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Jun Li, Wang Han, Yau Wei Yun, and School of Electrical and Electronic Engineering
- Subjects
Engineering ,Phase portrait ,business.industry ,Fingerprint (computing) ,Mechanical engineering ,Computer vision ,Artificial intelligence ,Engineering::Electrical and electronic engineering::Electronic systems::Biometrics [DRNTU] ,business - Abstract
In recent decades, automatic fingerprint identification system has attracted significant interest among the scientific research community. Though large amount of effort has been made in both the academic and industrial communities, there are still unsolved problems. Noisy image processing and large scale database processing are two typical problems. Fingerprint images consist of oriented texture. Therefore the orientation information plays a very important role in fingerprint processing. Moreover, the orientation of the fingerprint follows a certain structure that is not random. Such oriented structure provides a possibility to recover the orientation information in fingerprints corrupted by noise, even for a large noise patch which cannot be solved by the traditional gradient based methods. Accurate reconstruction of the orientations is useful for fingerprint filtering, segmentation, classification and recognition. Several methods have been proposed to reconstruct the fingerprint orientation. In this thesis, phase portrait model approach is used. The first order phase portrait is demonstrated to be robust for both occluded and noisy fingerprints. It also has the capability to describe the orientation patterns of the core point and delta point in a fingerprint image. Therefore a method based on the first order phase portrait is proposed to reconstruct the local orientation near the core and delta points. Furthermore, considering the regularity of the fingerprint orientation and linearity of the first order phase portrait, a model based on the piecewise phase portrait is used to predict the orientations far away from the singular point region. The advantage of this prediction model is that it provides a way to overcome the large patch of noise or missing orientation. To obtain the fingerprint orientation with higher precision, more complicated model based on the constrained nonlinear phase portrait approach is proposed. The model employs the advantages of both the first order and high order phase portrait model. Hence accurate reconstructed orientations at both the local and global area can be obtained simultaneously. This thesis also addresses fingerprint classification using the coefficients of the constrained nonlinear phase portrait model together with the singularities information. Since the coefficients of the constrained nonlinear phase portrait model play a key role in reconstructing the fingerprint orientation, it is also expected to be able to effectively classify fingerprints. A fingerprint classification algorithm using the orientation model and the singular points' information which complement the orientation information is developed and is shown to have good classification performance. DOCTOR OF PHILOSOPHY (EEE)
- Published
- 2019
172. Pose-invariant action recognition for automated behaviour analysis
- Author
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Manoj Ramanathan, Yau Wei Yun, Teoh Eam Khwang, and School of Electrical and Electronic Engineering
- Subjects
Engineering::Electrical and electronic engineering [DRNTU] - Abstract
Computer vision deals with providing visual capabilities to a computer so that it can understand its surrounding environment. This has given rise to numerous applications such as human-computer interactions, object detection, scene understanding, surveillance etc. Understanding human behaviour is a difficult task due to its fickle nature. A person’s action is one of the visible clues towards one’s behaviour or intention. Due to this, research in action recognition has gained momentum in recent years. Actions can be mainly characterized by the motion of body parts and evolution of the pose of the person. In an unconstrained setting, achieving a clear and distinct action representation is very difficult due to pose variations caused as a result of variation in camera view angle changes, occlusion of body parts, cluttered background etc. Also, traditional action recognition datasets portray actions that have subjects mainly in upright postures such as walking, running, boxing etc. There is lack of study related non-upright posture action recognition. Thus in this research, we develop a novel pose-invariant action recognition which combines both motion of body parts and pose of the person in a mutually reinforcing manner. The proposed method is generalized to handle possible human posture variations (including non-upright postures). The assumption made is that the neck point of the person and major viewing direction of the person during motion are available in the frame. Our pose-invariant action recognition framework is inspired from closed loop framework of control system theories with two components, namely, pose-invariant motion feature extraction component (Forward path) and canonical pose component (Feedback path) with the output of each used as input to the other to reinforce the initial prediction in a manner that improves the overall final result. The two components of our proposed action recognition framework are • Pose-Invariant Motion Component or Propagation Motion Forward (PMF) Path In this component, we aim to capture motion of different body parts that characterizes an action. To achieve this, a propagation mechanism is used to divide the region of interest is divided into 3 grids corresponding to the head, torso and legs respectively based on the estimated body orientation of the person in the frame. To capture the motion in each grid, we use a set of kinematic features derived from optical flow and encode them in a pose-invariant manner by converting them to a human-body centric space. Using these initial set of extracted invariant kinematic motion features, we provide initial hypothesis of possible actions. • Canonical Pose Component or Canonical Pose Feedback (CPF) Path The initial action recognized based on invariant kinematic motion features are used as input by this component. Each action is characterized by specific poses, we call these as canonical stick poses. Given an action and available training videos, we propose an algorithm to extract normalized canonical stick poses that can be compared with the test videos. For comparing these stick poses with the visual data, we propose a novel pose hypothesis generation scheme that compares each of the extracted canonical sticks of the action recognized by the first component with the video frame to identify the most likely canonical stick pose in it. The identified canonical stick pose in the frame is used to improve the pose-invariant motion feature extraction by realigning the grids used in the first component and also compute another kinematic motion feature of each body part with respect to the center of that body part. To capture temporal dynamics of action, identified stick poses are used to compute a new set of temporal stick features. The new set of pose-invariant motion features extracted from both forward and feedback paths using the realigned grids serves as the final action representation. Using these features, action hypothesis is refined until the action converges to yield the recognized action. In our canonical pose feedback path, to compare each canonical stick pose with the test frame, we propose a canonical pose hypothesis scheme that uses a body part detector as it’s basis. Most body part detectors or person detectors available work based on the assumption that the person is in an upright posture. Therefore, we propose our own body part detector that can work even when the person is in non-upright postures. In our body part detector, we do not impose any connectivity or shape constraints between the parts allowing us to detect body parts in non-upright postures. We divide the human body into 4 body parts head, torso, arm and leg. For detecting these body parts, training images were collected from the web using a python crawler. Using the training images, body part classifiers were trained. Based on the Bayes’ theorem for conditional probability, we propose an algorithm to compute the likelihood scores for each of the detected parts in the frame. The algorithm uses the developed classifier’s output to determine a likelihood score for the detected body parts. This likelihood score is used by the pose hypothesis generation scheme of the canonical pose component. To test our method’s effectiveness under non-upright posture, we introduce a new action dataset comprising of 8 actions in different postures captured from 3 views. The dataset comprises of visual data for 35 subjects. We have conducted several experiments on publicly available benchmark action datasets such as Weizmann, KTH, UCF Sports, Hollywood, HMDB51 and this new dataset to test the effectiveness of our recognition framework. The results show that the proposed reinforcement framework for action recognition is pose-invariant, partially view-invariant and is able to work even if there is partial occlusion of the person performing the action. Doctor of Philosophy (EEE)
- Published
- 2017
173. Unsupervised face analysis from multi-view
- Author
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Seyed Mohammad Hassan Anvar, Teoh Eam Khwang, Yau Wei Yun, School of Electrical and Electronic Engineering, and A*STAR Institute for Infocomm Research
- Subjects
Engineering ,Engineering::Electrical and electronic engineering::Electronic systems::Signal processing [DRNTU] ,business.industry ,Speech recognition ,Face analysis ,business - Abstract
Face detection, localization and recognition from multi-poses are one of the most challenging topics in the area of computer vision and pattern recognition. During the past decade several methods have been proposed for face detection and recognition but they mainly concentrate on frontal faces. Although in recent years, some methods have been proposed based on sliding windows to exhaustively search through the image in many scales and views, they are slow and their performance on multi-view or multi-pose faces are not as good as frontal face detections. Training these methods is also a bottleneck. Most of the proposed methods require thousands of positive and negative samples from different face poses for training. These images for training have to be manually cropped, aligned and labeled, thus is manually intensive and expensive to perform. Note that since the effect of variation in the face pose captured on a fix camera and change in camera view but with a fixed face pose is the same, we used both words interchangeably, In this research project, a probabilistic approach is proposed for face detection, localization and identification from multi-views. The proposed method does not supervision during the training stage. The main focus of the project is on multi-view analysis as well as unsupervised or automated learning. The proposed approach provides a unified framework to learn a multi-view model for face detection, localization and identification. For face detection and localization from multi poses, given the images of different people in multi-poses, it obtains a multi-view face model obtained through a constellation of corresponding face features in the training set. The obtained model is pruned such that only the most distinctive features are retained. The model is then used to detect and localize multiple face images in multi-poses. Two versions of model construction are proposed for face detection and localization. One requires manually labeling only two control points in one image of the training set regardless of the number of images in the training set. The other version is completely automatic. Even the control points are estimated automatically and the system only requires some training images that include face of different people from multi-views. The trade-off is that the accuracy is slightly lower compared to the manually labeled approach. Another effort is face identification and localization from multi poses. The proposed method is completely automatic. The data collection is also performed by the system using web images obtained from web search. It requires only the textual name of the queried person, which is used as input to the search engine. Using the images returned by the search engine, the system classifies these images and constructs a multi-view face model for the query. With the model, the system is then able to identify the query candidate in his/her digital photo album or gallery. The user does not have to manually tag the images or provide images to train the face recognition module. Since system is completely automatic, it has widely application including search for people in news video or tagging actors and actresses in movies or targeted advertisement for interactive televisions (IPTV). DOCTOR OF PHILOSOPHY (EEE)
- Published
- 2014
174. Robust representation and recognition of facial emotions
- Author
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Seyedehsamaneh Shojaeilangari, Teoh Eam Khwang, Yau Wei Yun, and School of Electrical and Electronic Engineering
- Subjects
Engineering::Electrical and electronic engineering::Control and instrumentation [DRNTU] - Abstract
Facial Emotion detection under natural conditions is an interesting topic with a wide range of potential applications like human-computer interaction. Although there is significant research progress in this field, there are still challenges related to real-world unconstrained situations. One essential challenge is to find pose invariant spatio-temporal volumetric features to analyze the video sequence efficiently. Another important issue is how to deal with noisy and imperfect data recorded in uncontrolled environments such as illumination variations, partial occlusion, and head movements. The focus of this research is to develop a robust system for facial expression recognition as a dynamic event in natural situations. Two strategies have been proposed in this research to address the uncontrolled environments related challenges: Robust representation framework: we propose a novel spatio-temporal descriptor based on Optical Flow (OF) components which is very distinctive and also pose-invariant. Robust recognition framework: we explored the effectiveness of sparse representation obtained by supervised learning a set of basis (dictionary). Extreme Sparse Learning (ESL) is proposed to jointly learn a dictionary and a nonlinear classification model to robustly detect the facial expression in real-world natural situations. The proposed approach combines the discriminative power of the Extreme Learning Machine (ELM) with the reconstruction property of the sparse representation to deal with noisy signal and imperfect data recorded in natural settings. Since the facial feature extraction performance is highly dependent on facial pose, we propose a novel spatio-temporal descriptor which is robust to facial pose variations. However, the feature encoding may fail in the presence of extreme head pose variations, where some parts of the face are not visible in the recorded images. To address this problem and also dealing with illumination variations and occlusion, we suggested following the idea of sparse representation where the noisy data can be reconstructed from the clean data provided by the dictionary of the sparse representation. While the sparse representation approach has the ability to enhance noisy data using a dictionary learned from clean data, it is not sufficient because the end goal is to correctly recognize the facial expression. In a sparse-representation-based classification task, the desired dictionary should have both representational ability and discriminative power. Since separating the classification training from dictionary learning may cause the learned dictionary to be sub-optimal for the classification task, we propose to jointly learn a dictionary and classification model. In other words, in contrast with most existing schemes that attempt to update the dictionary and classifier parameters alternately by iteratively solving each sub-problem, we propose to solve them simultaneously. This joint dictionary learning and classifier training can be expected to result in a dictionary that is both reconstructive and discriminative for a robust recognition system. To the best of our knowledge, this is the only work that attempts to simultaneously learn the sparse representation of the signal and train a nonlinear classifier to be discriminative for sparse codes. The proposed method jointly learns a single dictionary and also an optimal nonlinear classifier. We have performed extensive experiments on both acted and spontaneous emotion databases to evaluate the effectiveness of the proposed feature extraction and classification schemes under different scenarios. Our results clearly demonstrate the robustness of the proposed emotion recognition framework, especially in challenging scenarios that involve illumination changes, occlusion, and head pose variations. DOCTOR OF PHILOSOPHY (EEE)
- Published
- 2014
175. Gaussian processes for pattern recognition applications
- Author
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Gao Yan, Chan Kap Luk, Yau Wei Yun, and School of Electrical and Electronic Engineering
- Subjects
symbols.namesake ,Engineering::Electrical and electronic engineering::Electronic systems::Signal processing [DRNTU] ,business.industry ,Computer science ,Speech recognition ,Pattern recognition (psychology) ,symbols ,Pattern recognition ,Artificial intelligence ,business ,Gaussian process - Abstract
Gaussian process (GP) is a stochastic process that has been studied for a long time and gained wide interests in the machine learning community in recent years. In this thesis, several interesting pattern analysis problems are solved using Gaussian process. Gaussian process models can be interpreted in two views, the weight space view and the function space view. Their different interpretations can be helpful in applying GPs to solve real problems. Its capability as functional prior inspired the original work on learning nonparametric similarity measure in this thesis. The similarity between pairwise inputs is considered a smooth function described by a GP prior. Given known similarity constraints, the model can be tuned by maximum likelihood and similarity on new inputs can be inferred. The advantage is that the learned similarity measure does not assume any parametric form. It is flexible to model various data with arbitrary distributions, and handles noise and outliers in the data. Gaussian process latent variable model (GPLVM) is a data modeling tool that is derived based on Gaussian processes from the weight space view. It was originally proposed for visualization of high dimensional data. DOCTOR OF PHILOSOPHY (EEE)
- Published
- 2009
176. Human pose estimation based on data-driven Monte Carlo hidden Markov models
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Meng Tao, Yau Wei Yun, Chua Chin Seng, and School of Electrical and Electronic Engineering
- Subjects
Engineering::Computer science and engineering::Computing methodologies [DRNTU] ,Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering [DRNTU] - Abstract
Estimating human poses from 2D images or video sequences can provide the moving trajectories of the body joints for the high level processing, human activity recognition, which is applicable in surveillance, human-computer interaction and clinical and sport analysis. This project proposes a new statistical formulation called the data-driven Monte Carlo hidden Markov model to estimate human poses from random initializations. MASTER OF ENGINEERING (EEE)
- Published
- 2007
177. Robust human detection within a highly dynamic aquatic environment in real time.
- Author
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Eng HL, Wang J, Wah AH, and Yau WY
- Subjects
- Algorithms, Artificial Intelligence, Cluster Analysis, Computer Systems, Humans, Image Enhancement methods, Information Storage and Retrieval methods, Reproducibility of Results, Sensitivity and Specificity, Biometry methods, Image Interpretation, Computer-Assisted methods, Movement, Pattern Recognition, Automated methods, Swimming, Video Recording methods, Water
- Abstract
This paper presents a real-time foreground detection method for monitoring swimming activities at an outdoor swimming pool. Robust performance and high accuracy of detecting objects-of-interest are two central issues of concern. Therefore, in this paper, a considerable amount of attention has been placed on the following aspects: 1) to establish a better method of modeling aquatic background, which exhibitis dynamic characteristics with random spatial movements, and 2) to establish a method of enhancing the visibility of the foreground by removing specular reflection at nighttime. First, the development of a new background modeling method is reported. In the proposed approach, the background is modeled as a composition of homogeneous blob movements. With an implementation of a spatial searching process, the proposed method shows capability in associating and distinguishing movements caused by the background. Hence, this contributes to better performance in foreground detection. On the issue of enhancing the visibility of the foreground, a decision-based filtering scheme is proposed as a preprocessing step. A defined concept term, fluctuation measure, is defined for classifying each pixel to be one of the predefined types. This has allowed suitable spatial or spatiotemporal filters to be applied accordingly for color the compensation step. All of these developments are evaluated by testing live on a busy Olympic-size outdoor public swimming pool. Both qualitative and quantitative evaluations are reported. This provides a comprehensive study of the system.
- Published
- 2006
- Full Text
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