| Summary: | 碩士 === 國立交通大學 === 電控工程研究所 === 102 === Human face recognition system is a desired technique in our daily life, such as the home nursing care system, security applications, and many others. It is a widely well-come technique that all-day-long and on-line to recognize a person from video camera. To this end, we use a near infrared (NIR) cameras to capture day-and-night video images for on-line human recognition.
In this thesis, 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. Moreover, the image contrast color tones of video frames in the night is worse than in the day, thus we employ multi-scale retinex to enhance video frames in the night before face extraction.
Despite OpenCV’s popularity to date, the technique to extract face sub-images from taken videos are not reliable. We can obtain many non-face sub-images among those obtained extracted face sub-images. We judiciously collect extracted sub-images those very far-away from to the centroids of persons to be classified and then remove them as non-face sub-images. This may remedy the shortcoming of OpenCV package, and greatly increase the face recognition rates.
Then the extracted face sub-image is transformed to a new space by eigenspace and canonical space transformation. The recognition is finally done in canonical space. Moreover, we consider most recent three consecutive face image recognition from video, and use majority vote to recognize the person. Moreover, we test face image recognition on two intruders, who do not belong to the members in the training set. Our proposed system can reject the intruder successfully.
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