In this paper, I present my face detection and tracking method. First, image enhancement is carried out in HSV space especially if the input image is acquired from unconstrained illumination condition. I used a method for image enhancement in HSV space based on the local processing of image. I propose a lighting invariant face detection system based upon the edge and skin tone information of the input color image. The advantage of the proposed face detection is that, it can detect faces with different size, pose, and expression under unconstrained illumination conditions. I combined the Kalman filter with Camshift to enable track recovery after occlusions and to avoid the tracking failures caused by objects and background with similar colors to face. In my tracking method, I particularly focus on face tracking. The size and position of window are obtained after Camshift iteration. Kalman filtering is used to predict the next starting iterative point of Camshift. The experimental results show that my tracking method get the better results than Camshift in occlusion sequences and dynamic backgrounds.

1. Introduction
 2. Effective face detection
  2.1. Image enhancement
  2.2. Skin color segmentation
  2.3. Labeling and face authentication
 3. Face tracking using hybrid filter
  3.1. Camshift algorithm
  3.2. Kalman filter
  3.3. Overview of my face trackingmethod
 4. Experimental results
 5. Conclusion
 References

• Kyoung-Bae Eum(Department of Computer and Information Engineering, Kunsan National University)