Face Detection in Close-up Shot Video Events Using Video Mining

Face detection and recognition in abrupt dynamic images is still challenging due to high complexity of images. To tackle this issue, we employed Gray-Level Co-occurrence Matrix (GLCM) to convert large video into smaller consequential sections containing sequence information from a series of images. GLCM is a matrix associated with the relationship between the values of adjacent pixels in an image. The proposed method is composed of two stages. First, the video is taken as input using the histogram difference method. Features are extracted using co-occurrence matrix of images, statistical methods, and the border of sudden shots extracted from the video. Second, face recognition with the Viola-Jones algorithm is performed on the sudden shots extracted in the first step. Thus, the face is extracted by video data mining in output in close shots. In this method, we compared the parameter model in three windows (3, 5 and 7) and threshold limit for detecting abrupt cuts between values (0.1, 0.5, 1.5, 1.5 and 2) for each window. The highest percentage of face detection is attained by considering the maximum percentage of abrupt cuts in the 5×5 window with a threshold value of 1.