We have proposed a very simple yet highly reliable face recognition algorithm using Adjacent Pixel Intensity Difference Quantization (APIDQ) histogram previously. In this paper, we present a modified quantization method to improve recognition performance. 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 r-θ 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. 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.