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Grandmother cell is a term in neuroscience to imitate the simplistic notion that the brain has a separate neuron to represent every familiar face, with important properties of sparseness and invariance. This paper proposes a linear regression based classification model for face recognition, which learn a mapping from the training feature vectors to the grandmother-cell-like codes, with one unit corresponding...
Face images always have significant intra-class variations due to different poses, illuminations and facial expressions. These variations trigger substantial deviation from the linearity assumption of data structure, which is essential in formulating linear dimension reduction technique. In this paper, we present a kernel based regularized graph embedding dimension reduction technique, known as kernel-based...
Classifier design plays a paramount role in pattern recognition. Previously, we set the problem in the framework of function approximation, wherein a classifier is assumed to be an element of a reproducing kernel Hilbert space continuously defined on the pattern feature space, and adopted orthogonal projection criteria for classifier design. In practice, subspaces spanned by features of different...
Due its possibilities in security systems and robotics, face recognition is one of the most researched areas within the biometric field. In a common scenario from real life face recognition problem, the dimension in the sample space is larger than the number of training samples per class. This is known as the “small sample size problem”. Discriminative Common Vectors (DCV) technique has been used...
The use of quality measures in biometrics is rapidly becoming the standard strategy for improving performance of biometric systems, especially in the presence of variable environmental conditions of signal capture. It is often necessary to integrate multiple quality measures into the classification process in order to capture the relevant aspects of signal quality. The inclusion of multiple quality...
The paper introduces a feature extraction technique for face recognition called the phase-based Gabor Fisher classifier (PBGFC). The PBGFC method constructs an augmented feature vector which encompasses Gabor-phase information derived from a novel representation of face images - the oriented Gabor phase congruency image (OGPCI) - and then applies linear discriminant analysis to the augmented feature...
This paper proposes a pain expression recognition method using boosted Gabor features. At first, each neonatal facial image which is normalized to the size of 112times92 pixels is convoluted with the 2D Gabor filters to extract 412160 Gabor features. Since the high-dimensional Gabor feature vectors are quite redundant, we propose a modified version of AdaBoost algorithm, called the HybridBoost, to...
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