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We propose in this letter two new subspace learning methods, called nearest feature space analysis (NFSA) and discriminant nearest feature space analysis (DNFSA), for pattern classification. While many subspace learning algorithms have been proposed in recent years, most of them apply the conventional nearest neighbor (NN) metric to derive the subspace and may not effectively characterize the geometrical...
The face recognition problem is made difficult by the great variability in head rotation and tilt, lighting intensity and angle, facial expression, aging, partial occlusion (e.g. Wearing Hats, scarves, glasses etc.), etc. In this paper two multi scale techniques Discrete Cosine Transform and Discrete Wavelet Transform are used. Discrete Cosine Transform is applied by retaining various levels of DCT...
Interactive Digital TV has arisen new forms of interaction not traditionally associated with this medium. One of its applications is the so-called “t-learning” or TV-based interactive learning. To date, most existing t-learning applications confine themselves to edutainment more than to more complex and pedagogical forms of learning, due to the technological constraints imposed by set-top boxes. This...
In this work, we proposed a novel authentication system based on facial features. The proposed method is based on PCA and LDA for feature extraction, these extracted features are combined using wavelet fusion. In this work we use neural networks to classify extracted features of faces. The proposed method consists of six steps: i) Extraction of images from the database, ii) Preprocessing, iii) Feature...
Towards building a multimodal affect recognition system, we have built a facial expression recognition system and a audio-lingual affect recognition system. In this paper, we present and discuss the development and evaluation process of the two subsystems, concerning the recognition of emotions from audio-lingual and visual-facial modalities. Many researchers agree that these modalities are complementary...
Motivated by the fact that a class has its own optimal feature vector discriminating itself from the other classes, we propose a new feature extraction algorithm for face recognition. In the scheme of the proposed class-dependent LDA (CDLDA), the evaluation criterion discriminating the samples of one class from those of the other classes is proposed. Different linear transformation spaces are constructed...
Illumination and expression variation are the major challenges in the face recognition. This paper presents comparative analysis of two normalization techniques namely, DCT in Log domain and 2-point normalization method.. The DCT is employed to compensate for illumination variations in the logarithm domain. Since illumination variation lies mainly in the low frequency band, an appropriate number of...
Recently diverse studies have emerged on the ubiquitous environment including the ones on RFID tag. In terms of security, in particular, RFID tag is not able to provide complex forms of security services due to its hardware limits, putting in turn limits on its wider use. Therefore, this thesis is to suggest an improved RFID tag authentication scheme using face recognition scheme. It divides passive...
Contemporary 2D face recognition is still a challenging work, especially when lighting varies. Thus, many works of resolving illumination variation in face recognition have been proposed, in the past decades. In this paper, we proposed Wavelet Local Binary Patterns Histogram Specification as a preprocessing technique for illuminated face recognition. Based on wavelet analysis, an illuminated facial...
The sensitivity to illumination changes is one of the most important issues for the evaluation of face recognition systems. In this paper, we propose a new approach to recognize face images under variation of lighting conditions when only one sample image per person is available. In this approach, a face image is represented as an array of Patch PCA (PPCA) extracted from a partitioned face image containing...
Face recognition is one of the most active research areas in computer vision and pattern recognition with practical applications. This work proposes an appearance based eigenface technique. PCA is used in extracting the relevant information in human faces. In this method the eigenvectors of the set of training images are calculated which define the face space. Face images are projected on to the face...
This paper presents a new algorithm for LDA-based face recognition with selection of optimal principal components using E-coli Bacterial Foraging Strategy (EBFS). A GA-PCA algorithm has been reported to find optimal eigenvalues and corresponding eigenvectors in LDA. In their paper, a fitness function has been proposed to find the optimal eigenvectors to be used in LDA using a Genetic Algorithm (GA)...
Principal component analysis (PCA) and Fisher discriminate analysis (FDA) of holistic approach of Information theory have been analyzed. Two steps for recognition are taken: training and testing. In the training phase a set of the eigenvectors of the covariance matrix of the images used for training. These eigenvectors are also called as eigenfaces. In testing phase when a new input image is given...
In this paper we propose fast face recognition system based on the Kekre's transform. This algorithm can be easily implemented and number of coefficients required for recognition reduces drastically compared to benchmark algorithm PCA. Thus computational burden decreases. We have compared the performance of this transform with conventional transforms like DCT, DST, Slant transform and WHT. The algorithm...
The challenge of facial biometrics is the decision rule of how to determine whether the claimant is the genuine user. It is an important issue that the decision rule affects the accuracy of performance. Therefore, the study proposes a breadth-first-based decision algorithm for facial biometrics. The proposed algorithm searches different graph paths to obtain a verified decision to accept or reject...
In this paper, we propose an approach using spatial displacement (or residue) of facial points, for Facial Expression Recognition using 3D facial models. It is shown that residues of facial points, have more information about facial expressions than the static expressive face itself. This approach overcomes some of the inherent defects in just taking the expressive face as the basic model to work...
Automatic face recognition is a challenging problem, since human faces have a complex pattern. This paper presents a technique for recognition of frontal human faces on gray scale images. In this technique, the distance between the Discrete Cosine Transform (DCT) of the face under evaluation and all the DCTs of the faces database are computed. The faces with the shortest distances probably belong...
In this paper, we propose an efficient method to compute the optimal discriminant vectors of Generalized Discriminant Analysis (GDA) for face recognition tasks. The optimal discriminative features of face images are obtained by directly performing the kernel Gram-Schmidt orthogonalization procedure on the difference vectors only once. The theoretical justification is presented. The nonlinear difference...
Facial symmetry can be regarded as a not absolute but useful and natural feature. In this paper, this symmetrical feature is applied to two-dimensional linear discriminant analysis (2DLDA) for face image feature extraction, furthermore, the distance measure (DM) and Frobenius-norm measure(FM) are also developed to classify faces. Symmetrical 2DLDA (S2DLDA) used pure statistical mathematical technique...
Obtaining invariant representation of time varying signals is one of the major problems in object recognition. Recently, a new method that slowly feature analysis (SFA) which can extract invariant features of temporally varying signals is being explored, which is an extension of independent component analysis (ICA) which has been used for extracting facial feature. The technique of SFA can be extended...
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