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51
Face recognition using kernel direct discriminant analysis algorithms
- IEEE Trans. Neural Networks
"... Abstract—Techniques that can introduce low-dimensional feature representation with enhanced discriminatory power is of paramount importance in face recognition (FR) systems. It is well known that the distribution of face images, under a perceivable variation in viewpoint, illumination or facial expr ..."
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Cited by 143 (12 self)
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Abstract—Techniques that can introduce low-dimensional feature representation with enhanced discriminatory power is of paramount importance in face recognition (FR) systems. It is well known that the distribution of face images, under a perceivable variation in viewpoint, illumination or facial expression, is highly nonlinear and complex. It is, therefore, not surprising that linear techniques, such as those based on principle component analysis (PCA) or linear discriminant analysis (LDA), cannot provide reliable and robust solutions to those FR problems with complex face variations. In this paper, we propose a kernel machine-based discriminant analysis method, which deals with the nonlinearity of the face patterns ’ distribution. The proposed method also effectively solves the so-called “small sample size ” (SSS) problem, which exists in most FR tasks. The new algorithm has been tested, in terms of classification error rate performance, on the multiview UMIST face database. Results indicate that the proposed methodology is able to achieve excellent performance with only a very small set of features being used, and its error rate is approximately 34 % and 48 % of those of two other commonly used kernel FR approaches, the kernel-PCA (KPCA) and the generalized discriminant analysis (GDA), respectively. Index Terms—Face recognition (FR), kernel direct discriminant analysis (KDDA), linear discriminant analysis (LDA), principle component analysis (PCA), small sample size problem (SSS), kernel methods. I.
Recent advances in visual and infrared face recognition—a review
- CVIU
, 2005
"... Abstract Face recognition is a rapidly growing research area due to increasing demands for security in commercial and law enforcement applications. This paper provides an up-to-date review of research efforts in face recognition techniques based on two-dimensional (2D) images in the visual and infr ..."
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Cited by 105 (9 self)
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Abstract Face recognition is a rapidly growing research area due to increasing demands for security in commercial and law enforcement applications. This paper provides an up-to-date review of research efforts in face recognition techniques based on two-dimensional (2D) images in the visual and infrared (IR) spectra. Face recognition systems based on visual images have reached a significant level of maturity with some practical success. However, the performance of visual face recognition may degrade under poor illumination conditions or for subjects of various skin colors. IR imagery represents a viable alternative to visible imaging in the search for a robust and practical identification system. While visual face recognition systems perform relatively reliably under controlled illumination conditions, thermal IR face recognition systems are advantageous when there is no control over illumination or for detecting disguised faces. Face recognition using 3D images is another active area of face recognition, which provides robust face recognition with changes in pose. Recent research has also demonstrated that the fusion of different imaging modalities and spectral components can improve the overall performance of face recognition.
Independent Component Analysis of Gabor Features for Face Recognition
- IEEE Transactions on Neural Networks
, 2003
"... We present in this paper an Independent Gabor Features (IGF) method and its application to face recognition. The novelty of the IGF method comes from (i) the derivation of independent Gabor features in the feature extraction stage, and (ii) the development of an IGF features-based Probabilistic Reas ..."
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Cited by 80 (2 self)
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We present in this paper an Independent Gabor Features (IGF) method and its application to face recognition. The novelty of the IGF method comes from (i) the derivation of independent Gabor features in the feature extraction stage, and (ii) the development of an IGF features-based Probabilistic Reasoning Model (PRM) classification method in the pattern recognition stage. In particular, the IGF method first derives a Gabor feature vector from a set of downsampled Gabor wavelet representations of face images, then reduces the dimensionality of the vector by means of Principal Component Analysis (PCA), and finally defines the independent Gabor features based on the Independent Component Analysis (ICA). The independence property of these Gabor features facilitates the application of the PRM method for classification. The rationale behind integrating the Gabor wavelets and the ICA is two-fold. On the one hand, the Gabor transformed face images exhibit strong characteristics of spatial locality, scale and orientation selectivity. These images can thus produce salient local features that are most suitable for face recognition. On the other hand, ICA would further reduce redundancy and represent independent features explicitly. These independent features are most useful for subsequent pattern discrimination and associative recall. Experiments on face recognition using the FERET and the Chengjun Liu is with the Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102. E-mail: liu@cs.njit.edu.
A neurobiological theory of automaticity in perceptual categorization
- Psychological Review
, 2007
"... A biologically detailed computational model is described of how categorization judgments become automatic in tasks that depend on procedural learning. The model assumes 2 neural pathways from sensory association cortex to the premotor area that mediates response selection. A longer and slower path p ..."
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Cited by 45 (8 self)
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A biologically detailed computational model is described of how categorization judgments become automatic in tasks that depend on procedural learning. The model assumes 2 neural pathways from sensory association cortex to the premotor area that mediates response selection. A longer and slower path projects to the premotor area via the striatum, globus pallidus, and thalamus. A faster, purely cortical path projects directly to the premotor area. The model assumes that the subcortical path has greater neural plasticity because of a dopamine-mediated learning signal from the substantia nigra. In contrast, the cortical-cortical path learns more slowly via (dopamine independent) Hebbian learning. Because of its greater plasticity, early performance is dominated by the subcortical path, but the development of automaticity is characterized by a transfer of control to the faster cortical-cortical projection. The model, called SPEED (Subcortical Pathways Enable Expertise Development), includes differential equations that describe activation in the relevant brain areas and difference equations that describe the 2- and 3-factor learning. A variety of simulations are described, showing that the model accounts for some classic single-cell recording and behavioral results.
Gabor wavelets and General Discriminant Analysis for face identification and verification
, 2007
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Permutation coding technique for image recognition systems
- IEEE Transactions on Neural Networks
"... Abstract—A feature extractor and neural classifier for image recognition systems are proposed. The proposed feature extractor is based on the concept of random local descriptors (RLDs). It is followed by the encoder that is based on the permutation coding technique that allows to take into account n ..."
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Cited by 12 (6 self)
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Abstract—A feature extractor and neural classifier for image recognition systems are proposed. The proposed feature extractor is based on the concept of random local descriptors (RLDs). It is followed by the encoder that is based on the permutation coding technique that allows to take into account not only detected features but also the position of each feature on the image and to make the recognition process invariant to small displacements. The combination of RLDs and permutation coding permits us to obtain a sufficiently general description of the image to be recognized. The code generated by the encoder is used as an input data for the neural classifier. Different types of images were used to test the proposed image recognition system. It was tested in the handwritten digit recognition problem, the face recognition problem, and the microobject shape recognition problem. The results of testing are very promising. The error rate for the Modified National Institute of Standards and Technology (MNIST) database is 0.44 % and for the Olivetti Research Laboratory (ORL) database it is 0.1%. Index Terms—Face recognition, handwritten digit recognition, MNIST database, Olivetti Research Laboratory (ORL) database, permutation coding neural classifier. I.
Face Recognition by Regularized Discriminant Analysis
"... Abstract—When the feature dimension is larger than the number of samples the small sample-size problem occurs. There is great concern about it within the face recognition community. We point out that optimizing the Fisher index in linear discriminant analysis does not necessarily give the best perfo ..."
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Cited by 9 (0 self)
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Abstract—When the feature dimension is larger than the number of samples the small sample-size problem occurs. There is great concern about it within the face recognition community. We point out that optimizing the Fisher index in linear discriminant analysis does not necessarily give the best performance for a face recognition system. We propose a new regularization scheme. The proposed method is evaluated using the Olivetti Research Laboratory database, the Yale database, and the Feret database. Index Terms—Face recognition, optimization, regularized discriminant analysis (RDA), small sample-size problem. I.
Facial Expression Classification Using PCA and Hierarchical Radial Basis Function Network *
"... Intelligent human-computer interaction (HCI) integrates versatile tools such as perceptual recognition, machine learning, affective computing, and emotion cognition to enhance the ways humans interact with computers. Facial expression analysis is one of the essential medium of behavior interpretatio ..."
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Cited by 7 (0 self)
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Intelligent human-computer interaction (HCI) integrates versatile tools such as perceptual recognition, machine learning, affective computing, and emotion cognition to enhance the ways humans interact with computers. Facial expression analysis is one of the essential medium of behavior interpretation and emotion modeling. In this paper, we modify and develop a reconstruction method utilizing Principal Component Analysis (PCA) to perform facial expression recognition. A framework of hierarchical radial basis function network (HRBFN) is further proposed to classify facial expressions based on local features extraction by PCA technique from lips and eyes images. It decomposes the acquired data into a small set of characteristic features. The objective of this research is to develop a more efficient approach to discriminate between seven prototypic facial expressions, such as neutral, smile, anger, surprise, fear, disgust, and sadness. A constructive procedure is detailed and the system performance is evaluated on a public database “Japanese Females Facial Expression (JAFFE). ” We conclude that local images of lips and eyes can be treated as cues for facial expression. As anticipated, the experimental results demonstrate the potential capabilities of the proposed approach.
Classspecific kernel-discriminant analysis for face verification
- IEEE T-IFS
"... Abstract—In this paper, novel nonlinear subspace methods for face verification are proposed. The problem of face verification is considered as a two-class problem (genuine versus impostor class). The typical Fisher’s linear discriminant analysis (FLDA) gives only one or two projections in a two-clas ..."
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Cited by 6 (5 self)
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Abstract—In this paper, novel nonlinear subspace methods for face verification are proposed. The problem of face verification is considered as a two-class problem (genuine versus impostor class). The typical Fisher’s linear discriminant analysis (FLDA) gives only one or two projections in a two-class problem. This is a very strict limitation to the search of discriminant dimensions. As for the FLDA for class problems ( is greater than two), the transformation is not person specific. In order to remedy these limitations of FLDA, exploit the individuality of human faces and take into consideration the fact that the distribution of facial images, under different viewpoints, illumination variations, and facial expression is highly complex and nonlinear, novel kernel-dis-criminant algorithms are proposed. The new methods are tested in the face verification problem using the XM2VTS, AR, ORL, Yale, and UMIST databases where it is verified that they outperform other commonly used kernel approaches such as kernel–PCA (KPCA), kernel direct discriminant analysis (KDDA), complete kernel Fisher’s discriminant analysis (CKFDA), the two-class KDDA, CKFDA, and other two-class and multiclass variants of kernel-discriminant analysis based on Fisher’s criterion. Index Terms—Face verification, Fisher’s linear discriminant analysis (FLDA), kernel techniques, two-class problems. I.
Face Recognition Using DCT and Hybrid Flexible Tree
- In Proc. of the International Conference on Neural Networks and Brain
, 2005
"... Abstract—This paper proposes a new face recognition approach by using the Discrete Cosine Transform (DCT) and hybrid flexible neural tree (FNT) classification model. The DCT is employed to extract the input features to build a face recognition system, and the flexible neural tree is used to identify ..."
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Cited by 5 (1 self)
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Abstract—This paper proposes a new face recognition approach by using the Discrete Cosine Transform (DCT) and hybrid flexible neural tree (FNT) classification model. The DCT is employed to extract the input features to build a face recognition system, and the flexible neural tree is used to identify the faces. Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input features selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using an evolutionary algorithm and the parameters are optimized by a particle swarm optimization algorithm. Empirical results indicate that the proposed framework is efficient for face recognition. I.
