Access control is vital to prevent adversary from stealing resources from data centres. The security of traditional authentication means, such as password and Personal Identification Number PIN, are imperfect for access control. In this paper, a reliable facial biometric access control with promising authentication performance is proposed. In our study, facial feature representation is computed based on ICA modelling, descriptor binarization, bitwise operation on the bit maps and effective compression via whitening PCA. The proposed technique is namely Binarized Independent Component Pattern BICP. BICP training module integrates ICA methodology to construct ICA filter bank from natural image patches. Each face image is convoluted with the filters for the corresponding ICA responses. The ICA responses are further processed via feature binarization, and XOR bitwise operation before convert to code map. Next, block-wise histogramming is applied on each code map. By concatenating the regional histograms, it produces a set of high dimensional BICP descriptor, which will be further scaled and compressed. Empirical results show the remarkable performance of BICP on facial expression, illumination, time span and facial makeup effects.
['Ying Han Pang', 'Ean Yee Khor', 'Shih Yin Ooi']
Biometric Access Control with High Dimensional Facial Features