Multi-descriptor random sampling for patch-based face recognition

While there has been a massive increase in research into face recognition, it remains a challenging problem due to conditions present in real life. This paper focuses on the inherently present issue of partial occlusion distortions in real face recognition applications. We propose an approach to tackle this problem. First, face images are divided into multiple patches before local descriptors of Local Binary Patterns and Histograms of Oriented Gradients are applied on each patch. Next, the resulting histograms are concatenated, and their dimensionality is then reduced using Kernel Principle Component Analysis. Once completed, patches are randomly selected using the concept of random sampling to finally construct several sub-Support Vector Machine classifiers. The results obtained from these sub-classifiers are combined to generate the final recognition outcome. Experimental results based on the AR face database and the Extended Yale B database show the effectiveness of our proposed technique

Face recognition with occlusion using dynamic image-to-class warping (DICW)

A novel approach Dynamic Image-to-Class Warping (DICW) is proposed to deal with partially occluded face recognition in this work. An image is partitioned into subpatches, which are then concatenated in the raster scan order to form a sequence. A face consists of forehead, eyes, nose, mouth and chin in a natural order and this order does not change despite occlusion or small rotation. Thus, in this work, a face is represented by the aforementioned sequence which contains the order of facial features. Taking the order information into account, DICW computes the distance between a query face and an enrolled person by finding the optimal alignment between the query sequence and all sequences of that person along both time dimension and within-class dimension. Extensive experiments on public face databases with various types of occlusion have verified the effectiveness of the proposed method. In addition, our method, which considers the inherent structure of the face, performs with greater consistency than current methods when the number of enrolled images per person is limited. Our method does not require any training process and has a low computational cost, which makes it applicable for real-world FR applications