Robust facial feature point detection under nonlinear illuminations

Addresses the problem of facial feature point detection under different lighting conditions. Our goal is to develop an efficient detection algorithm, which is suitable for practical applications. The problems that we need to overcome include (1) high detection accuracy, (2) low computational time and (3) nonlinear illumination. An algorithm is developed and reported in the paper. One of the key factors affecting the performance of feature point detection is the accuracy in locating face boundary. To solve this problem, we propose to make use of skin color, lip color and also the face boundary information. The basic idea to overcome the nonlinear illumination is that, each person shares the same/similar facial primitives, such as two eyes, one nose and one mouth. So the binary images of each person should be similar. Again, if a binary image (with appropriate thresholding) is obtained from the gray scale image, the facial feature points can also be detection easily. To achieve this, we propose to use the integral optical density (IOD) on face region. We propose to use the average IOD to detect feature windows. As all the above-mentioned techniques are simple and efficient, the proposed method is computationally effective and suitable for practical applications. 743 images from the Omron database with different facial expressions, different glasses and different hairstyle captured indoor and outdoor have been used to evaluate the proposed method and the detection accuracy is around 86%. The computational time in Pentium III 750 MHz using matlab for implementation is less than 7 seconds.