Weighted Performance comparison of DWT and LWT with PCA for Face Image Retrieval

This paper compares the performance of face image retrieval system based on discrete wavelet transforms and Lifting wavelet transforms with principal component analysis (PCA). These techniques are implemented and their performances are investigated using frontal facial images from the ORL database. The Discrete Wavelet Transform is effective in representing image features and is suitable in Face image retrieval, it still encounters problems especially in implementation; e.g. Floating point operation and decomposition speed. We use the advantages of lifting scheme, a spatial approach for constructing wavelet filters, which provides feasible alternative for problems facing its classical counterpart. Lifting scheme has such intriguing properties as convenient construction, simple structure, integer-to-integer transform, low computational complexity as well as flexible adaptivity, revealing its potentials in Face image retrieval. Comparing to PCA and DWT with PCA, Lifting wavelet transform with PCA gives less computation and DWT-PCA gives high retrieval rate.. Especially ‘sym2’ wavelet outperforms well comparing to all other wavelets.