Designing a video-based face detection and recognition system
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hiendtr@hcmute.edu.vnKeywords:
Video frame, Haar-Like feature, Face recognition, Principal component analysis (PCA), Artificial Neural network (ANN), Eigenvector, EigenfaceAbstract
Face recognition in videos has been a hot topic in computer vision in recent years. Compared to traditional face analysis, video-based face recognition has the advantages of more abundant information to improve accuracy and robustness, but also suffers from large scale variations, low quality of facial images, illumination changes, pose variations and occlusions. The paper presents a method for face recognition based on video-image based methods. The proposed method consists of three stages: face detection using Haar-Like feature, feature extraction using principle component analysis, and recognition using the feed forward back propagation Neural Network. The algorithm has been tested on a video with 1000 frames (1000 images). Test results gave a recognition rate of 98%
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