人脸识别Face Detection C library with Skin and Motion analysis.ppt

BIOMETRICS AIA 2007 TTS Conference BIOMETRICS AIA 2007 TTS Conference BIOMETRICS AIA 2007 TTS Conference BIOMETRICS AIA 2007 TTS Conference BIOMETRICS AIA 2007 TTS Conference Face Detection C++ library with Skin and Motion analysis Yuriy V. Chesnokov (/audio/face_detection.asp) BIOMETRICS AIA 2007 TTS Conference The first step in intelligent image\video processing for face recognition in uncontrolled scenery with complex background (outdoor environments, airports, train\bus stations) is face detection. Upon precision of the later heavily depends your face recognition results. The project demonstrates face detection SSE optimized C++ library for color and gray scale data with skin detection, motion estimation for faster processing, small sized support vector machine (SVM) and artificial neural network (ANN) rough face prefiltering, PCA\LDA\ICA\any dimensionality reduction\projection and final ANN classification. It detects multiple faces on conventional desktop PC with 2.0Ghz processor at least at 15fps on 640x480 RGB video stream. With motion and skin estimation detection rate could be as high as 50fps. BIOMETRICS AIA 2007 TTS Conference The algorithm Downscale the image with Haar transform to 80x60 to reduce search space, remove high freq noise, convert it to gray scale. Estimate motion vector to reduce search space. Detect skin regions (optional) and combine search region with motion vector. Build image pyramid from 80x60 image and smooth the data with Gaussian filter. Browse through histogram equalized 19x19 rectangles on the image pyramid in the search space only. Reject non-face rectangles with small sized classifiers: 2 support vectors linear SVM, 3 layered ANN with 2-4 hidden neurons. Project positive face rectangles to PCA/LDA/ICA or any other linear projection basis to reduce dimensionality of 19x19 = 361 dimensional image vector. Classify the projected 19x19 rectangles with ANN classifier. Estimate the ANN output on image pyramid an