Development Of Face Detection And Alignment Algorithm Based On GPU

With the development of face recognition technology in recent years, computer vision has been applied to this field deeply. Face localization is the basis of face recognition, containing face detection and face alignment.This thesis focuses on the joint face localization algorithm development, using shape indexed feature and transformed decision tree to do face detection and face alignment at the same time, and GPU parallel computing using CUDA. All the work mainly focuses on three aspects.1) Development of the joint face localization algorithm, including construction of the classification and regression tree by shape indexed feature, and the training of classifiers using Adaboost and cascade. In the algorithm implementation process, in order to enhance the representation of shape indexed feature based on pixel, this thesis uses HSV image and gradient image to expand the original grey image channel and get the shape indexed feature of multiple channels.2) To be more efficient, this study makes the time and parallel feasibility analysis for optimizing the classification and regression tree constructing progress by CUDA on GPU.3) In the detection, this thesis makes further analysis on parallel feasibility and accomplishes the parallel computation optimization on sub-window.This thesis gets impressive results with 90% precision on AFW dataset and a mean residual of 3.92 when the dataset containing 20 key-points. This thesis makes the detection on VGA image runs at 34fps,17 times faster than on CPU in efficiency. For a 2000 training samples with 1000 features, the construction time of classification and regression tree is reduced from 258 seconds to 100 seconds.