Abstract
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.
Highlights
Face images can be captured at a distance and can be used in various applications including surveillance, tracking, access control, etc
This paper has proposed a novel face recognition algorithm using the concept of random patching
Local Binary Patterns (LBPs) operator is employed as a local descriptor and combined with HOG technique to extract a concatenated descriptor of the image patches
Summary
Face images can be captured at a distance and can be used in various applications including surveillance, tracking, access control, etc. Face modality has been widely investigated in the biometric research field compared to other biometric modalities such as iris, fingerprint, and palmprint counterparts. The human face can be accurately recognised in a restricted environment. In an unrestricted environment, several challenges are encountered where faces are exposed to distortions. These distortions include illumination changes, pose variations, and partial occlusion. While multiple algorithms have been proposed to tackle them in recent years, they have their limitations or requirements that cannot be met for faces in the wild
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