Face Recognition Using Adjacent Pixel Intensity Difference Quantization Histogram

This paper presents a very simple yet highly reliable face recognition algorithm using Adjacent Pixel Intensity Difference Quantization (APIDQ) histogram. At each pixel location in an input image, a 2-D vector (composed of the horizontally adjacent pixel intensity difference (dIx) and the vertically adjacent difference (dIy)) contains information about the intensity variation angle (theta) and its amount (r). After the intensity variation vectors for all the pixels in an image are calculated and plotted in the r-theta plane, each vector is quantized in terms of its. and r values. By counting the number of elements in each quantized area in the r-theta plane, a histogram can be created. This histogram, obtained by APIDQ for facial images, is utilized as a very effective personal feature. In this paper, we optimize the quantization method of APIDQ according to the maximum entropy principle (MEP), and determine the best parameters for APIDQ. Experimental results show maximum average recognition rate of 97.2% for 400 images of 40 persons from the publicly available face database of AT&T Laboratories Cambridge. Furthermore, by utilizing rough location information of facial parts, the facial area is divided into 5 individual parts, and then APIDQ is applied on each facial component. Recognition results are firstly obtained from different parts separately and then combined by weighted averaging. The experimental result shows that top 1 recognition rate of 97.6% is achieved when evaluated by FB task of the FERET database.