A Hybrid Feature Extraction Framework for Face Recognition

Abstract

A hybrid framework, that can be used in face recognition applications, to enhance system recognition efficiency and speed by extracting the most efficient features of face images is proposed. The proposed framework is based on features obtained using Histograms of Oriented Gradients (HOG) descriptor and compressive sensing (CS). The HOG feature descriptor has the advantage of extracting face feature vectors even with changes in face appearance and is fully capable of handling variations in illumination. CS is used to reduce the density of the resulting HOG face features which has a significant effect on improving the computational cost and performance of the system. For classification, the k-Nearest Neighbors (k-NN) algorithm and Probabilistic Neural Network (PNN) classifier are used. The results demonstrated that the proposed hybrid method could be implemented in a complete system for recognizing and identifying faces with varying illuminations, facial expressions and poses, and backgrounds in real time.