Eigen domain transformation for soft-margin multiple feature-kernel learning for surveillance face recognition

Abstract

Face Recognition (FR) is the most accepted method of biometric authentication due to its inherent passive nature. This has attracted a lot of researchers over past few decades to achieve an moderately high accuracy under controlled environments. In order to achieve such an accuracy for FR under surveillance scenario has been proved to be a major hurdle in this area of research, mainly due to the difference in resolution, contrast, illumination and camera parameters of the training and the testing samples. In this paper, we propose a novel technique to find the optimal feature-kernel combination by SML MFKC (Soft-margin Learning for Multi-Feature-Kernel Combination) to solve the problem of FR in surveillance, followed by an Eigen Domain Transformation (EDT) to bridge the gap between the distributions of the gallery and the probe samples. Rigorous experimentation has been performed on three real-world surveillance face datasets: FR SURV [24], SCface [17] and ChokePoint [35]. Results have been shown using Rank-1 Recognition rates, ROC and CMC measures. Our proposed method outperforms all other recent state-of-the-art techniques by a considerable margin. Experimentations also show that the recent state-of-the-art Deep Learning techniques also fail to perform appreciably compared to our proposed method for the afore-mentioned datasets.