Study of robust facial recognition under occlusion using different techniques

摘要

This paper represents the different techniques used for the study of robust facial recognition. The need of facial recognition is expanding very rapidly in current technologies as it is possible to identify a humane face features through different digital mediums. Although, facial recognition is not done achieved easily in the real-world situation as most of the time the human face goes through various occlusions making hard for the system to complete the identification process. The sole purpose of this article is to compare the techniques used and identify the most accurate and efficient one from all of them. Robust facial recognition is a task of doing recognition under uncertain environments and features. The main aim of the robust facial recognition is to analyze and identify the face images where the picture proposes difficult viewpoints, angles, poses, illumination, noises and expressions. The techniques studied for this article are Local Binary Patterns, HOG features, Occlusion-adaptive Deep Networks (ODN), Robust Principal Component Analysis (RPCA), Progressive Convolutional Neural Network and Region Attention Networks (RAN). In this article, a table is created summarizing the experiments and results of the techniques studied.