Multi-view face detection - Yang, Ziwei (2018)
In order to improve the speed and robustness of multi-view face detection, this dissertation proposes a face detection method called multi-view face detection based on asymmetric principal component analysis (APCA) and support vector machine (SVM) classifier. APCA is proposed to remove the unreliable dimensions more effectively than the conventional PCA. Targeted at the two-class problem, an asymmetric discriminant analysis in the APCA subspace is proposed to regularize the eigenvalue which is a biased estimate of the variance in the corresponding dimension. In the training phase, five SVM linear classifiers are trained by using the positive database with different angles. Then APCA is applied on each group after classification. In the test phase, the linear classifier is used to quickly determine the different angles of faces, and then the Bhattacharyya distance is applied on asymmetric DA to further verify the face area of each group, thus to detect the face. The experimental results show the effectiveness and correctness of the proposed method.