A Robust Face Recognition Algorithm Using Markov Stationary Features and Adjacent Pixel Intensity Difference Quantization Histogram
Feifei Lee, Koji Kotani, Qiu Chen, Tadahiro Ohmi · 2011
In this paper, we present a robust face recognition algorithm using adjacent pixel intensity difference quantization (APIDQ) histogram combined with Markov Stationary Features (MSF). Previously, we have proposed a very simple yet highly reliable face recognition algorithm using Adjacent Pixel Intensity Difference Quantization (APIDQ) histogram. After the intensity variation vectors for all the pixels in an image are calculated, each vector is quantized directly in (dIx, dIy) plane instead of polar plane. By counting the number of elements in each quantized area in the (dIx, dIy) 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 combine the APIDQ histogram with MSF so as to add spatial structure information to histogram. Experimental results show maximum average recognition rate of 97.16% is obtained for 400 images of 40 persons from the publicly available face database of AT&T Laboratories Cambridge. Furthermore, Top 1 recognition rate of 98.2% is achieved by using FB task of the publicly available face database of FERET.