An integrated multispectral face recognition system

摘要

A face recognition system usually consists of one recognition algorithm by processing single spectral images. For example, face pattern byte (FPB) algorithm was initially created using thermal (LWIR) images, while Elastic Bunch Graphic Matching (EBGM) algorithm was originated with visible (RGB) images. When there are two or more recognition algorithms and/or spectral images available, system performance can be enhanced using information fusion. In this paper, a score fusion with multispectral images is proposed to improve system performance, which is termed as an integrated multispectral face recognition system. Score fusion actually combines several scores from multiple matchers (algorithms) and/or multiple modalities (multispectra). The system performance is measured by the recognition accuracy (AC; the higher the better) and false accept rate (FAR; the lower the better). Specifically, a fusion method will combine the face scores from three matchers (Circular Gaussian Filter, FPB, EBGM) and from two-spectral bands (visible and thermal). We present and compare the system performance using seven fusion methods: linear discriminant analysis (LDA), k-nearest neighbor (KNN), support vector machine (SVM), binomial logistic regression (BLR), Gaussian mixture model (GMM), artificial neural network (ANN), and hidden Markov model (HMM). Our experiments are conducted with the Alcon State University multispectral face dataset that currently consists of two spectral images from 105 subjects. The experimental results show that the KNN score fusion produces the best performance (AC = 98.98%; FAR = 0.35%); and the SVM yields the second best. Compared with the performance of the single best matcher (AC = 91.67%, FAR = 8.33%), the integrated system with score fusion highly improves the accuracy, meanwhile dramatically reduces the FAR.