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Moving image processing uses continuous moving images such as recording live video as a data set. The process of developing a unique database catalog application of student face recognition begins with storing the results of real time recordings of each face. Face tracking uses the CamShift algorithm to identify skin color based on the Hue histogram value. To bring out the special features of a face image that has been convoluted to the kernel, the Gabor Wavelet method is used. 2D Gabor Wavelet kernel which is obtained by modulating 2D sine waves at certain frequencies and orientations with a gaussian envelope. Training data taken from the live video of student activities consists of 30 data sets. Stages start from file conversion - tracking video to multiple frames - RGB-HSV conversion - Calculate Histogram - Face detection and finally face recognition process. If there are similarities with the largest value between querying the face image in the database, the face will be detected. Looping is done to take the face of other students. Video .mp4 is converted to .avi. The results of video conversion are tracked into several frames according to facial movements followed by RGB - HSV conversion, 1-2 minute duration videos produce 40-250 frames. Face recognition successfully identifies moving faces around 80%-90% with skin color detection and 80%-85% with points position detection.

Real Time Catalog of Uniqueness Face Using the CAMShift and Gabor Wavelet Algorithims