Logarithmic Fourier PCA: A New Approach to Face Recognition

Lakshmiprabha Nattamai Sekar, Jhilik Bhattacharya, and Somjyoti Majumder


Face Recognition, Principal Component Analysis, Independent Component Analysis, Neural networks, Fourier transforms


This paper proposes two face recognition algorithms namely Logarithmic Fourier Domain Principal Component Analysis (Log Fourier PCA) and Log fourier PCA with Independent Component Analysis (ICA) which successfully tackles multiple variations of face images. Neural network is used as classifier for both these methods. The Log Fourier PCA method proves to be resilient against illumination variations of the face images. However it is observed that the performance of this method decreases if there are multiple variations. Using ICA as a feature extractor further makes the recognition system robust to multiple parametric variations. Experimental results using Yale, ORL, FERET and PIE database shows that the proposed method Log fourier PCA followed by ICA and Neural network can tackle multiple variations in the face images. Their comparisons with other hybrid systems such as ICA-Neural Network and Fourier PCA-Neural network methods are also presented.

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