This report provides a fast and reliable system for real-time face detection and recognition in complex backgrounds. Most current face... Show moreThis report provides a fast and reliable system for real-time face detection and recognition in complex backgrounds. Most current face recognition systems identify faces under constrained conditions, such as constant lighting condition, the same background. In the real world, people need to be recognized in complex backgrounds under different conditions, such as tilted head poses, various facial expressions, dark or strong lighting conditions. Meanwhile, because of large amounts of real-time applications for face recognition, such as intelligent robot, unmanned vehicle, security monitor, the fast face recognition rate needs to be satisfied for the real-time requirement. In this project, a fast and reliable system is designed to real-time detect and recognize faces under various conditions. Frames are obtained directly from VGA camera. Image preprocessing and face detection, collection, recognition are sequentially implemented on the frames. Local binary patterns and Haar features are used for face detection and eye detection. Local binary pattern encodes every pixel of the image for texture extraction, which is several times faster than Haar feature detection. Adaptive boosting algorithm is used for selecting the best weak classifiers and cascading method divides the extracted best classifiers into several stages to enhance detection rate. Affine transformation is implemented to unify the size of detected facial images and align two eyes to the desired position for improving recognition accuracy. 33 Gaussian filter is designed to remove noises of the pre-processed facial images. Principal component analysis (PCA) is used for face recognition, which is fast to identify high-dimensional faces with few principal components. M.S. in Electrical Engineering, July 2015 Show less
