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17 May 2012 Near real-time face detection and recognition using a wireless camera network
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We present a portable wireless multi-camera network based system that quickly recognizes face of human subjects. The system uses low-power embedded cameras to acquire video frames of subjects in an uncontrolled environment and opportunistically extracts frontal face images in real time. The extracted images may have heavy motion blur, small resolution and large pose variability. A quality based selection process is first employed to discard some of the images that are not suitable for recognition. Then, the face images are geometrically normalized according to a pool of four standard resolutions, by using coordinates of detected eyes. The images are transmitted to a fusion center which has a multi-resolution templates gallery set. An optimized double-stage recognition algorithm based on Gabor filters and simplified Weber local descriptor is implemented to extract features from normalized probe face images. At the fusion center the comparison between gallery images and probe images acquired by a wireless network of seven embedded cameras is performed. A score fusion strategy is adopted to produce a single matching score. The performance of the proposed algorithm is compared to the commercial face recognition engine Faceit G8 by L1 and other well known methods based on local descriptors. The experiments show that the overall system is able to provide similar or better recognition performance of the commercial engine with a shorter computational time, especially with low resolution face images. In conclusion, the designed system is able to detect and recognize individuals in near real time.
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Francesco Nicolo, Srikanth Parupati, Vinod Kulathumani, and Natalia A. Schmid "Near real-time face detection and recognition using a wireless camera network", Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 83921I (17 May 2012);

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