Human face recognition system is a desired technique in our daily life. It is a widely well-come technique that can all-day-long and on-line recognize a person from video cameras. To this end, we use a near infrared (NIR) camera to capture day-and-night video images for on-line human recognition. In this paper, we adopt human face sub-image attraction package in OpenCV, which is based on Haar cascade classifier. The package is a feature-based algorithm and works much faster than the pixel-based algorithm. It is to be noted that the image contrast color tones of video frames in the night is worse than that in the day, thus we employ multi-scale retinex to enhance video frames in the night before OpenCV face extraction routine. The extracted face sub-image is first transformed to a new space by eigenspace and canonical space transformation. The recognition is finally done in canonical space. Despite OpenCV's popularity to date, extracting face sub-images from taken videos are still not reliable enough. Namely, we can obtain many non-face sub-images among the extracted face sub-images. We judiciously classify the sub-images that are far away from the centroids of persons to be classified as non-face sub-images. This may remedy the shortcoming of OpenCV package, and greatly increase the face recognition accuracy. Furthermore, we consider the most recent three consecutive face image recognitions from video, and use majority vote to recognize a person to enhance the accuracy. Besides, we have tested face image recognition to reject intruders successfully.
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