얼굴인증 방법들의 조명변화에 대한 견인성 비교 연구

Abstract

This paper focuses on the study of the face authentication system and the robustness of face authentication methods under illumination changes. Four different face authentication methods are tried. These methods are as follows; PCA(Principal Component Analysis), GMM(Gaussian Mixture Models), 1D HMM(1 Dimensional Hidden Markov Models), Pseudo 2D HMM(Pseudo 2 Dimensional Hidden Markov Models). Experiment results involving an artificial illumination change to face images are compared with each other. Face feature vector extraction based on the 2D DCT(2 Dimensional Discrete Cosine Transform) is used. Experiments to evaluate the above four different face authentication methods are carried out on the ORL(Olivetti Research Laboratory) face database. Experiment results show the EER(Equal Error Rate) performance degrade in all occasions for the varying δ. For the non-illumination changes, Pseudo 2D HMM is 2.54%, 1D HMM is 3.18%, PCA is 11.7%, GMM is 13.38%. The 1D HMM have the better performance than PCA where there is no illumination changes. But the 1D HMM have worse performance than PCA where there is large illumination changes(δ≥40). For the Pseudo 2D HMM, The best EER performance is observed regardless of the illumination changes.