Siamese local and global networks for robust face tracking
journal contributionposted on 2020-09-29, 09:24 authored by Y Qi, S Zhang, F Jiang, Huiyu Zhou, D Tao, X Li
Convolutional neural networks (CNNs) have achieved great success in several face-related tasks, such as face detection, alignment and recognition. As a fundamental problem in computer vision, face tracking plays a crucial role in various applications, such as video surveillance, human emotion detection and human-computer interaction. However, few CNN-based approaches are proposed for face (bounding box) tracking. In this paper, we propose a face tracking method based on Siamese CNNs, which takes advantages of powerful representations of hierarchical CNN features learned from massive face images. The proposed method captures discriminative face information at both local and global levels. At the local level, representations for attribute patches (i.e:, eyes, nose and mouth) are learned to distinguish a face from another one, which are robust to pose changes and occlusions. At the global level, representations for each whole face are learned, which take into account the spatial relationships among local patches and facial characters, such as skin color and nevus. In addition, we build a new largescale challenging face tracking dataset to evaluate face tracking methods and to facilitate the research forward in this field. Extensive experiments on the collected dataset demonstrate the effectiveness of our method in comparison to several state-of-the art visual tracking methods.
National Natural Science Foundationof China (Nos. 61902092 and 61872112), National Key Research and Devel-opment Program of China (Nos. 2018YFC0806802 and 2018YFC0832105),and Fundamental Research Funds for the Central Universities GrantNo.HIT.NSRIF.2020005
CitationIEEE Transactions on Image Processing, 2020, https://doi.org/10.1109/TIP.2020.3023621
Author affiliationSchool of Informatics
- AM (Accepted Manuscript)