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Multi‐task cascaded convolutional neural network (MTCNN) is a human face detection architecture which uses a cascaded structure with three stages (P‐Net, R‐Net and O‐Net). The authors intend to reduce the computation time of the whole process of the MTCNN. They find that the non‐maximum suppression (NMS) processes after the P‐Net occupy over half of the computation time. Therefore, the authors propose a self‐fine‐tuning method which makes the control of computation time for the NMS process easier. Self‐fine‐tuning is a training trick which uses hard samples generated by P‐Net to retrain P‐Net. After self‐fine‐tuning, the distribution of human face probabilities generated by P‐Net is changed, and the tail of distribution becomes thinner. The control of the number of NMS input boxes can be made easier when the distribution has a thinner tail, and choosing a suitable threshold to filter the face boxes will generate less boxes. So the computation time can be reduced. In order to keep the performance of MTCNN, the authors still propose a landmark data set augmentation, which can enhance the performance of the self‐fine‐tuned MTCNN. From the experiments, it is found that the proposed scheme can significantly reduce the computation time of MTCNN. [ABSTRACT FROM AUTHOR]

Recently, a large family of supervised or unsupervised manifold learning algorithms that stem from statistical or geometrical theory has been designed to solve the problem of pattern classification. In this study, consider the fact that the data are usually sampled from a low‐dimensional manifold space which resides in a high‐dimensional Euclidean space, the authors propose a novel two‐graph‐based supervised linear classification algorithm called neighbourhood sensitive preserving embedding (NSPE). Different from local linear embedding (LLE) (or neighbourhood preserving embedding (NPE)) which preserves the local neighbourhood structure with one graph, NSPE can discover both the intrinsic and discriminant structure of the data manifold by constructing two graphs, that is, the within‐class graph and the between‐class graph. Thus, the data are mapped into a subspace where the nearby points with the same label are close to each other, whereas the nearby points with different labels are far apart. As a classification method, besides being defined on training samples, NSPE is also defined on testing samples. Experiments carried on the real‐world face databases demonstrate that the results of all two‐graph‐based spectral methods are comparable and better than that of one‐graph‐based methods. [ABSTRACT FROM AUTHOR]