A Near-Infrared Face Detection and Recognition System Using ASM and PCA+LDA

Near-infrared (NIR) images have a lot of advantages, which can make up the shortages of visible light (VL) images. A novel NIR detection and recognition system is presented in this paper. First, a NIR imaging system is developed to provide good illumination conditions for subsequent face detection and recognition. Then, Active Shape Model (ASM) method is applied to detect the features and the low-dimensional Gabor features are extracted for recognition by combining Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The recognition rate of the system is up to 90% in a few milliseconds. Finally, by using such real-time NIR face recognition system, comparative results are provided, from that we can seen that this system can work robustly and effectively

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