A color based face detection system using multiple templates

A color based system using multiple templates was developed and implem ented for detecting human faces in color images. The algorithm consists of three image processing steps. The first step is human skin color statistics. Then it separates skin regions from non-skin regions. After that, it locates the fronta l human face(s) within the skin regions. In the first step, 250 skin samples from persons of different ethnicities are used to determine the color distribution o f human skin in chromatic color space in order to get a chroma chart showing lik elihoods of skin colors. This chroma chart is used to generate, from the origina l color image, a gray scale image whose gray value at a pixel shows its likelih ood of representing the skin. The algorithm uses an adaptive thresholding proces s to achieve the optimal threshold value for dividing the gray scale image into separate skin regions from non skin regions. Finally, multiple face templates ma tching is used to determine if a given skin region represents a frontal human fa ce or not. Test of the system with more than 400 color images showed that the re sulting detection rate was 83%, which is better than most color-based face dete c tion systems. The average speed for face detection is 0.8 second/image (400×300 pixels) on a Pentium 3 (800MHz) PC.

A multiple template approach for robust tracking of fast motion target

Target tracking is very important in computer vision and related areas. It is usually difficult to accurately track fast motion target with appearance variations. Sometimes the tracking algorithms fail for heavy appearance variations. A multiple template method to track fast motion target with appearance changes is presented under the framework of appearance model with Kalman filter. Firstly, we construct a multiple template appearance model, which includes both the original template and templates affinely transformed from original one. Generally speaking, appearance variations of fast motion target can be covered by affine transformation. Therefore, the affine tr templates match the target of appearance variations better than conventional models. Secondly, we present an improved Kalman filter for approx- imate estimating the motion trail of the target and a modified similarity evaluation function for exact matching. The estimation approach can reduce time complexity of the algorithm and keep accuracy in the meantime. Thirdly, we propose an adaptive scheme for updating template set to alleviate the drift problem. The scheme considers the following differences： the weight differences in two successive frames; different types of affine transformation applied to templates. Finally, experiments demonstrate that the proposed algorithm is robust to appearance varia- tion of fast motion target and achieves real-time performance on middle/low-range computing platform.