Abstract Shadow detection is an important branch of computer vision. Recently, convolutional neural network (CNN)‐based methods for shadow detection have achieved better performance than methods based on manually designed features. However, CNNs are extremely hungry for data and the training of CNN‐based shadow detector requires time‐consuming and expensive pixel‐level annotations. To alleviate this problem in shadow detection, a method of data augmentation based on generative adversarial network (GAN), named ShadowGAN, has been proposed. Given a shadow mask and a shadow‐free image, our ShadowGAN can generate shadow images with labels. To guide the training of ShadowGAN and get more realistic shadow images, loss is further implemented to impose a restriction between real shadow images and generated shadow images. The effectiveness of ShadowGAN is demonstrated by training existing shadow detectors on enlarged dataset. In addition, to better make use of shadow‐free images in shadow detection, shadow image classification task is added for the shadow detectors. Experiments show that this task can guide the feature extraction network to learn more robust shadow features. At last, these two methods are combined and a better performance of shadow detection is achieved.

Depression is a severe psychiatric disorder preventing a person from functioning normally in both work and daily lives. Currently, diagnosis of depression requires extensive participation from clinical experts. It has drawn much attention to develop an automatic system for efficient and reliable diagnosis of depression. Under the influence of depression, visual-based behavior disorder is readily observable. This paper presents a novel method of exploring facial region visual-based nonverbal behavior analysis for automatic depression diagnosis. Dynamic feature descriptors are extracted from facial region subvolumes, and sparse coding is employed to implicitly organize the extracted feature descriptors for depression diagnosis. Discriminative mapping and decision fusion are applied to further improve the accuracy of visual-based diagnosis. The integrated approach has been tested on the AVEC2013 depression database and the best visual-based mean absolute error/root mean square error results have been achieved.

Overweight and obesity is quite common in the modern society, which can result in many severe health problems. Thus weight loss has become a major event for many people to have a healthy living. A question is then raised for Biometrics or identity management: Is there any influence on face recognition when the facial shapes are varied, caused by body weight changes? No previous research has addressed this issue, to the best of our knowledge. In this paper, we study the influence of body weight changes on face recognition. Both synthesized and real face images are assembled as the databases to facilitate our study. Empirically, we found that large body weight alterations can significantly reduce the matching accuracy of the face recognition system. This is a new exploration to the biometrics society. Then we study if the influence of weight changes can be reduced to improve the face recognition performance. The partial least squares (PLS) method is applied for this purpose. Our preliminary results show that it is feasible to develop algorithms to address the influence of facial adiposity variation on face recognition, caused by weight changes.

Recent studies have shown that facial cosmetics have an impact on face recognition. To develop a face recognition system that is robust to facial makeup, we propose performing correlation mapping between makeup and nonmakeup faces on features extracted from local patches. Three methods are explored to learn the correlations. We also study the problem of makeup detection. Four categories of features are proposed to characterize cosmetics, including skin color tone, skin smoothness, texture, and highlight. A patch selection scheme and discriminative mapping are presented to enhance the performance of makeup detection. A complete system is then developed for face verification utilizing the makeup detection result. Experimental results show that our system is robust to cosmetics in face authentication. An accuracy of about 80.0% can be achieved on a database of about 500 pairs of makeup and nonmakeup face images.