Abstract: The existing face recognition algorithms used in intelligent monitoring system are mainly applied to short distance images, which still have the problem of low face recognition rate when being applied to long distance images because the image quality decreases as the distance grows. To improve the face recognition accuracy in long distance images, a novel face recognition algorithm was proposed based on using mixed multiple different distance images and Linear Discriminant Analysis (LDA). The proposed algorithm used mixed images extracted from multiple different distances to train images, and used bilinear interpolation method to normalize the image set, and then uses the Manhattan distance to measure the similarity of images to realize the face recognition. The experimental results demonstrate that, compared to the traditional algorithm based on short single distance image and LDA, the proposed algorithm can improve significantly the face recognition rate 6.2% in short distance and 31% in long distance respectively.

Key words: intelligent surveillance system, face recognition, linear discriminant analysis, bilinear interpolation method, manhattan distance, face recognition rate