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In this paper, a new approach which is called the common matrix approach is proposed for face recognition. The common matrix for each class can be calculated either using Gram-Schmidt orthogonalization method or using scatter matrix of each class. In both ways, orthonormal mat rices in the indifference subspace represent the directions that contain important discriminative information. The proposed...
We propose a new framework for image recognition by selectively pooling local visual descriptors, and show its superior discriminative power on fine-grained image classification tasks. The representation is based on selecting the most confident local descriptors for nonlinear function learning using a linear approximation in an embedded higher dimensional space. The advantage of our Selective Pooling...
The collaborative representation-based classifier (CRC) is proposed as an alternative to the sparse representation based classifier (SRC) for image face recognition. CRC solves an l2-regularized least squares formulation, with algebraic solution, while SRC optimizes over an I1-regularized least squares problem. As an extension of CRC, the weighted collaborative representation-based classifier (WCRC)...
In this paper a framework is presented to deals with various aspects of face recognition like illumination, rotation and scaling. The proposed framework consists of three parts. In the first part Gabor filter is used over the thermal faces at different scales, locations, and orientations. In second part, the fixed point algorithm Fast ICA have been used over the Gabor filtered images to represent...
Most of classification methods including the ones based on sparse representation (SRC), look at every training sample and its extracted modalities as a single point in a high dimensional space and a collection of these points build the training space used to train the classifier. In a multimodality classification problem, there might be lots of redundancies associated with different modalities of...
In this paper, we present a novel approach for recognition of human faces using Markov Random Fields (MRF) and Bayesian models. We examine the relationship between feature vectors in a close proximity system. The feature vectors are coefficients of the 2D Gabor Wavelet Transform (DWGT). The MRF is implemented to match the constraint configurations between the feature vectors. The MRFs posterior probability...
The appearance of the face varies drastically when background and pose change. Variations in these conditions make Face Recognition (FR) an even more challenging and difficult task. In this paper we propose two novel techniques, viz., Gabor-Feature-based DFT Shifting (GFDS) and Skin-detection-based Background Removal, to improve the performance of the FR system. GFDS is used to detect and neutralize...
Holistic approaches of face recognition are not robust to illumination, scale, occlusion and age variations. Various studies indicate that the performance of holistic approaches degrades as the face database size increases. In this paper, we propose a user specific landmark geometry based approach that assigns weights to different geometrical distances according to their role in face recognition process...
The goal of this paper is to present a critical comparison of existing classical techniques on recognition of human faces. This paper describes the four major classical face recognition techniques i.e., i) Principal Component Analysis (PCA), ii) Linear Discriminant Analysis (LDA), iii) Discrete Cosine Transform (DCT), and iv) Independent Component Analysis (ICA). Strong and weak features of these...
Extracting robust and discriminatory features from images is a crucial task for infrared face recognition. For this reason, we have developed an infrared face recognition algorithm based on improved local features, which applies adaptive threshold quantization to encode the local directional energy. The conventional LBP-based feature as represented by the fix threshold encoding has limited distinguishing...
Face recognition in extreme situations is still challenging to researchers. While several algorithms have shown great recognition results in ideal conditions, accuracy decreases when recognition tasks present a high illumination variation. In this paper, we propose to add two components to the recognition system to make the surf descriptor efficient in such extreme situations. First, we learn a discriminant...
In face recognition, the illumination variation problem in uncontrolled environments has gained some research activities. Although the quotient image based methods are reported to be a simple yet practical technique in face recognition, these methods could not satisfactorily maximize the ratios of between-class and within-class scatter and may not effectively be used for the illumination variation...
In this paper, we present a rich image representation which is robust to illumination, facial expression and scale variations. For this aim, firstly, we propose a novel dense local image representation method based on Walsh Hadamard Transform (WHT) called Local WHT (LWHT). LWHT is the application of WHT to each pixel of an image to decompose it into multiple components, called LWHT maps. Secondly,...
Automatic facial expression recognition has been drawn many attentions in both computer vision and artificial intelligence (AI) for the past decades. Although much progress has been made, facial expression recognition (FER) is still a challenging and interesting problem. In this paper, we propose a new FER system, which uses the active shape mode (ASM) algorithm to align the faces, then extracts local...
In this article we address the issue of adopting a local sparse coding representation (Histogram of Sparse Codes), in a part-based framework for inferring the locations of facial landmarks. The rationale behind this approach is that unsupervised learning of sparse code dictionaries from face data can be an effective approach to cope with such a challenging problem. Results obtained on the CMU Multi-PIE...
Face Recognition (FR) under varying pose, illumination and expression (PIE) conditions is challenging, and extracting PIE-invariant features is an effective approach to solve this problem. For enhancing the performance of an FR system, this paper proposes a unique combination of Contourlet Transform (CT), Discrete Cosine Transform (DCT) and Binary Particle Swarm Optimization (BPSO). CT and DCT are...
In this paper, a new type of hybrid method that hybridizes PCA and EBGM as a two-stage procedure is presented to improve recognition performance in large-scale face recognition. Among various methods in face recognition, PCA is considered to identify human faces by holistic views, while EBGM is supposed to distinguish one face from another by details, but they are both excellent representative methods...
Eigenface is one of the most common appearance based approaches for face recognition. Eigenfaces are the principal components which represent the training faces. Using Principal Component Analysis, each face is represented by very few parameters called weight vectors or feature vectors. While this makes testing process easy, it also includes cumbersome process of generating eigenspace and projecting...
In this paper, the Gabor filter is studied and further expanded for temporal facial expression analysis. Originally, the Gabor feature describes both spatial and frequency characteristics of 2D images. The prominent of the theorem has been validated in research communities for a decade due to its similarity to the human perception system. The performance of the filter in the existing research gives...
Human face recognition technology is one of the hottest research in the field of pattern recognition at present. In this paper, the principle component analysis (PCA) and bidirectional principle component analysis (BDPCA) methods are proposed to recognize a grayscale face image, for which the size of the spatial distribution is 64 × 64. At first, the main part of the face is extracted to form the...
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