Abstract

Accurate and faster face identification task has always been the prominent task of any biometric based personnel identification system. However, due to high dimensional space of facial images, the computation tedious and time consuming. The computational cost can only be reduced if the dimensional space is reduced to at least 70% of the original image size. In the presented base work, it is observed that the eigen values are used to evaluate the features from the face under test. The eigen values are same in number as that of the size of the facial image. However, by using the principal component analysis, the eigen vector size is reduced. The reduced eigen vector size does not guarantee that only the principal feature from the facial images are extracted as it only reduces the image dimension. However, in the proposed work, we present a wavelet based approach where the facial image is divided into sub-bands and the HH-band iamge is used to identify the given image from the data base image. In the presented work, it is proposed to decompose the segmented facial image from the group photograph into LL, HL, LH and HH sub-bands using the haar wavelet. The HH sub-band image contains the maximum frequecny component of the facila image. The image is already reduced to a size of (N/2 x N/2) of actual size of NxN. Therefore, the speed of operation is fast enough as compared to other methods and without loss of high frequency components.

Authors and Affiliations