Enhanced TV-based Quotient Image Model and Its Application to Face Recognition with One Sample per Subject Employing Subspace Methods

In this paper, an Enhanced Total Variation based Quotient Image model (ETVQI) is described and discussed in detail. In the ETVQI model, a histogram equalization method is adopted to enhance contrast of samples. Then the TV-L 1 model is chosen to decompose samples to large-scale part u and small-scale part v. With the large-scale part, samples are further normalized by dividing them by u to normalize signals of intrinsic structures. Lastly, some feature fusion methods are adopted to generate the final normalized sample as the result of ETVQI. To apply ETVQI to face recognition, subspace analysis algorithms are suggested to perform subspace analysis on normalized samples. According to experiments on the CAS-PEAL face database, the face samples preprocessed by ETVQI could improve the performance of some famous subspace analysis algorithms (PCA, KPCA and ICA) with the standard testing sets proposed in the face database. Experimental results also confirm that samples preprocessed by ETVQI could make PCA, KPCA and ICA robust to not only lighting, but facial expression, masking, occlusion etc. in face recognition area

Scale-space and wavelet decomposition based scheme for face recognition using nearest linear combination.

Face recognition has attracted much attention from Artificial Intelligence researchers due to its wide acceptability in many applications. Many techniques have been suggested to develop a practical face recognition system that has the ability to handle different challenges. Illumination variation is one of the major issues that significantly affects the performances of face recognition systems. Among many illumination robust approaches, scale-space decomposition based methods play an important role in reducing the lighting effects in facial images. This research presents a face recognition approach for utilizing both the scale-space decomposition and wavelet decomposition methods. In most cases, the existing scale-space decomposition methods perform recognition, based on only the illumination-invariant small-scale features. The proposed approach uses both large-scale and small-scale features through scale-space decomposition and wavelet decomposition. Together with the Nearest Linear Combination (NLC) approach, the proposed system is validated on different databases. The experimental results have shown that the system outperforms many recognition methods in the same category. --Leaf ii.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b194710