Face Recognition Algorithm Using Two Dimensional Principal Component Analysis Based on Discrete Wavelet Transform

The roles of this paper is to improve the face recognition rate by applying different levels of discrete wavelet transform(DWT) as to reduce the high dimensional image into a low dimensional image. Two dimensional principal component analyze (2DPCA) is being utilized to find the face recognition accuracy rate, processing it through the ORL image database. This database contains images from 40 persons (10 different images for each) in grayscale and resolution of 92x112 pixels. An evaluation between 2DPCA and multilevel-DWT/2DPCA has been done. These have been assessed according to the recognition accuracy, recognition rate, dimensional reduction, computing complexity and multi-resolution data approximation. The results show that the recognition rate across all trials was higher using 2-level DWT/2DPCA than 2DPCA with a time rate of 4.28 sec. Also, these experiments indicate that the recognition time has been improved from 692.91sec to 1.69sec. with recognition accuracy from 90% to 92.5%.