Single sample face recognition using a network of spiking neurons

Abstract

Conventional face recognition methods usually assume the possession of multiple samples per person (MSPP) available for classification. This assumption however, may not hold in many practical face recognition applications since only single sample per person (SSPP) is available for enrollment. The scarcity in numbers of training sample could deteriorate the reliability of many popular face recognition methods. Thus, in this thesis, a novel semi-supervised face recognition approach is proposed to address the SSPP problem by effectively extracting the inherent information in face image through local ensembles strategy. A Spiking Neural Network (SNN) based classifier called Coincidence Detection SNN (CD SNN) is proposed which identifies the synchronization between input spikes and at the same time employs the psychophysically-relevant feature selection through synaptic time constant prediction (?_(s )Prediction) as bias for more accurate face classification. The CD SNN classifier is built on top an improved Zero-Order Spike Response Model (SRM0), utilizing spike time approximation using the proposed Output Spike Time Prediction (OSTP) approach for faster computation. The classification is then performed on more efficient and compact image representations acquired through SNN Face Descriptor (SNN FD). Comparisons with several state-of-the arts methods using several popular face datasets reveal that the proposed method can achieve equivalent performance under SSPP constraints, and in fact on several occasions, delivers significantly better performance than existing methods. Additionally, through a survey, it is found that proposed method performs better than human in SSPP face recognition. Based on the same survey, assessment on the difference of feature selection between human and proposed method is also presented.