Evolutionary multi-view face tracking on pixel replaced image in video sequence

Nowadays, many computer vision techniques are applied to practical applications, such as surveillance and facial recognition systems. Some of such applications focus on information extraction from the human beings. However, people may feel psychological stress about recording their personal information, such as a face, behavior, and cloth. Therefore, privacy protection of the images and videos is necessary. Specifically, the detection and tracking methods should be used on the privacy protected images. For this purpose, there are some easy methods, such as blurring and pixelating, and they are often used in news programs etc. Because such methods just average pixel values, no important feature for the detection and tracking is left. Hence, the preprocessed images are unuseful. In order to solve this problem, we have proposed shuffle filter and a multi-view face tracking method with a genetic algorithm (GA). The filter protects the privacy by changing pixel locations, and the color information can be preserved. Since the color information is left, the tracking can be achieved by a basic template matching with histogram. Moreover, by using GA instead of sliding window when the subject in the image is searched, it can search more efficiently. However, the tracking accuracy is still low and the preprocessing time is large. Therefore, improving them is the purpose in this research. In the experiment, the improved method is compared with our previous work, CAMSHIFT, an online learning method, and a face detector. The results indicate that the accuracy of the proposed method is higher than the others.