TIFd-FR: Trends, Issues and Future directions of feature extraction in Face Recognition

Abstract

Today, face recognition is the most prevalent and effective mechanism among various biometric technologies as it is non-invasive method. It helps in identifying or verifying the identity of a person by utilizing its face. But face recognition (FR) can be prone to high error rate. Therefore, efficient feature extraction methods are required for extracting robust facial features to develop efficient FR system. A FR system comprises of mainly three phases, that is, face detection and orientation, extracting facial features and classification of features. The most vital part of an efficient recognition system is extraction of robust features. Hence, extracting facial features is active research area of image processing. Although algorithms have been developed for extracting features, efficient and robust feature extraction still offers great challenge to researchers. Hence, a thorough analysis of feature extraction techniques presented in this work will enable the researchers to select the best suited technique for developing efficient system for recognition of face images. The performance of different feature extraction methods varies under variations in illumination, occlusion and pose etc. It is observed that deep-learning based feature extraction methods outperform wavelet-based methods. After rigorous analysis of various state-of-art techniques it is found that highest accuracy rate of 99.5% is achieved on AR database using wavelet based feature extraction whereas 99.78% is attained using convolutional neural network accuracy and various results achieved till now are stated.