Gabor feature based robust representation and classification for face recognition with Gabor occlusion dictionary

Abstract

By representing the input testing image as a sparse linear combination of the training samples via l 1 -norm minimization, sparse representation based classification (SRC) has shown promising results for face recognition (FR). Particularly, by introducing an identity occlusion dictionary to code the occluded portions of face images, SRC could lead to robust FR results against face occlusion. However, the l 1 -norm minimization and the high number of atoms in the identity occlusion dictionary make the SRC scheme computationally very expensive. In this paper, a Gabor feature based robust representation and classification (GRRC) scheme is proposed for robust FR. The use of Gabor features not only increases the discrimination power of face representation, but also allows us to compute a compact Gabor occlusion dictionary which has much less atoms than the identity occlusion dictionary. Furthermore, we show that with Gabor feature transformation, l 2 -norm could take place the role of l 1 -norm to regularize the coding coefficients, which reduces significantly the computational cost in coding occluded face images. Our extensive experiments on benchmark face databases, which have variations of lighting, expression, pose and occlusion, demonstrated the high effectiveness and efficiency of the proposed GRRC method.

Illumination Robust Face Recognition Based on Image Fusion of Local Contrast Enhancement and Adaptive Smoothing

“…Fig. 1 (c) shows the results after being smoothed and normalized by the conduction function 2 g . It can be seen that unrealistic images are produced.…”

“…where d and K are the same as ones in (2). To vertify that the new function new g hasn't edge sharpening effect, we suppose that there is a gray level on the edge from dark to bright.…”

“…In recent years, many scholars have been proposed numerous approaches for face recognition, such as Robust Principal Component Analysis (RPCA) [1], Gabor feature based robust representation and classification (GRRC) [2], iteratively reweighted regularized robust coding ( C IR 3 ) [3] and sparse representation based classification(SRC) [4]. All these methods have been applied successfully in public security, information security and financial fields.…”

Face recognition system based on principal components analysis and distance measures

“…It is based on the concept of dimensionality reduction and represent the correlated facial images into linearly uncorrelated Eigenfaces [9]. Most of the face recognition methods are either holistic based [10]- [11] or feature based methods [12]- [13]. In holistic based methods, the entire facial image is considered as input data.…”

Fast and robust face recognition via coding residual map learning based adaptive masking

“…Many related works have been developed, such as the l 1 -graph for image classification [8], kernel based SRC [9], Gabor feature based SRC [10,48], robust sparse coding (RSC) [24], robust alignment with sparse and low rank decomposition [25], joint dimension reduction and dictionary learning [49], face and ear multimodal biometric system [50], etc. In particular, the RSC method [24] has shown excellent results in FR with various occlusions.…”