Abstract
Face recognition is one of the hottest issues in the field of computer vision and pattern recognition. Deep learning-based recognition models already have more exceptional recognition ability than the human being on open datasets, but still cannot fully undertake the identity recognition task in real scenarios without human assistance. In this paper, we mainly analyze two obstacles, i.e., domain gap and training data shortage. We propose the unpaired Domain Transfer Generative Adversarial Network (DT-GAN) to relieve these two obstacles. We improve the GAN baseline to bridge the domain gap among datasets by generating images conforming to the style of a target domain by learning the mapping between the source domain and target domain. The generator could synthesize face with an arbitrary viewpoint at the same time. The model is trained with a combination of style transfer loss, identity loss, and pose loss, which ensures the successive domain transfer and data augment. We conduct experiments to testify the effectiveness and reasonability of DT-GAN. Experimental results demonstrate the recognition performance is dramatically boosted after domain transfer and data augment.
Highlights
Face recognition aims to figure out the identity of a specific face
Our contributions can be summarized into three aspects:(1) We propose a useful unpaired domain transfer model Domain Transfer Generative Adversarial Network (DT-Generative Adversarial Networks (GANs)), which can learn the mapping from the complete source domain to an incomplete target domain by adversarial-based learning method to narrow the domain gap; (2) DT-GAN can generate face images with various viewpoint as well as reserving identity for data augment resulting in a better performance in face recognition; (3) we conduct experiments and compare against several prior methods
STYLE TRANSFER To address the problem of performance drop caused by the domain gap, we propose the model DT-GAN to bridge the domain gap between the source domain and the target domain, which has the same structure with the objective in CycleGAN [18] and apply reversion loss to measure the mapping between domains
Summary
Because of its essential applications in the security and surveillance system, face recognition has been drawing lots of attention from both academia and industry. The performance of face recognition has been significantly boosted because of the development of deep learning. Different from face verification, face recognition is still not widely deployed in the surveillance system and obstacles remain to hinder the applications of face recognition. One of these open issues is the lack of suitable training data. The training data set is the base to train a deep learning model, and the quantity and quality of training data significantly impact the robust and generalization ability of deep learning models. The sources of training data mainly include self-established datasets and existing public datasets
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
