Abstract
Several methods have been proposed to describe face images in order to recognize them automatically. Local methods based on spatial histograms of local patterns (or operators) are among the best-performing ones. In this paper, a new method that allows to obtain more robust histograms of local patterns by using a more discriminative spatial division strategy is proposed. Spatial histograms are obtained from regions clustered according to the semantic pixel relations, making better use of the spatial information. Here, a simple rule is used, in which pixels in an image patch are clustered by sorting their intensity values. By exploring the information entropy on image patches, the number of sets on each of them is learned. Besides, Principal Component Analysis with a Whitening process is applied for the final feature vector dimension reduction, making the representation more compact and discriminative. The proposed division strategy is invariant to monotonic grayscale changes, and shows to be particularly useful when there are large expression variations on the faces. The method is evaluated on three widely used face recognition databases: AR, FERET and LFW, with the very popular LBP operator and some of its extensions. Experimental results show that the proposal not only outperforms those methods that use the same local patterns with the traditional division, but also some of the best-performing state-of-the-art methods.
Highlights
Face recognition is a popular biometric technique, mainly because it is considered as non-intrusive and it can be applied in a wide range of applications such as access control, video surveillance and human computer interaction [1]
The Local Binary Patterns (LBP) operator was first proposed for texture classification and was applied to face recognition using a regular regions division [9]
Some of the extensions aim at enhancing the discriminative capability of the operator, such as the improved LBP (ILBP) [10], in which both the pixels in the circular neighborhood and the center pixel are compared against the mean intensity value of them
Summary
Face recognition is a popular biometric technique, mainly because it is considered as non-intrusive and it can be applied in a wide range of applications such as access control, video surveillance and human computer interaction [1]. Some of the extensions aim at enhancing the discriminative capability of the operator, such as the improved LBP (ILBP) [10], in which both the pixels in the circular neighborhood and the center pixel are compared against the mean intensity value of them Another is the extended LBP (ELBP) [23], which encodes the gradient magnitude image in addition to the original image in order to represent the velocity of local variations. Afterwards, the histograms of all regions are concatenated into a single spatially enhanced feature histogram that encodes both the local texture and the global shape of face images Under this strategy, deciding the number and size of blocks is usually a problem, especially when there are different appearance variations on the face. We believe both, more robust descriptors and proper number of sets for each region, can boost the performance of this framework
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