Abstract
The face recognition scheme based on deep learning can give the best face recognition performance at present, but this scheme requires a large amount of labeled face data. The currently available large-scale face datasets are mainly Westerners, only containing few Asians. In practice, we have found that models trained using these data sets are lower in accuracy in identifying Asians than Westerners. Therefore, the establishment of a large-scale Asian face dataset is of great value for the development and deployment of face related applications for Asians. In this paper, we propose a simple semi-automatic approach to collect face images from Internet and build a large-scale Asian face dataset (AFD) containing 2019 subjects and 360,000 images. To the best of our knowledge, this is the largest Asian face image dataset proposed so far. To illustrate the quality of AFD, we train 3 different models with the same CNN structure yet by different training datasets (AFD, WebFace, mixed WebFace&AFD) and verify them on one Western and two Asian face testing datasets. Extensive experimental results show that the model by our AFD outperforms counterparts by a large margin for Asian face recognition. We have made the AFD dataset public to facilitate face recognition development for Asians.
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Acknowledgements
We’d like to thank Institute of Automation, Chinese Academy of Sciences (CASIA) for offering CASIA-FaceV5 and CASIA-WebFace datasets. The research was supported by National Natural Science Foundation of China (61671332, 61502354, U1736206), Hubei Province Technological Innovation Major Project (2017AAA123), National Key R&D Project (2016YFE0202300), Hubei Provincial Natural Science Fund Key Project (2018CFA024), and Joint Project of Yunnan Provincial Science and Technology Department (2017FH001-060).
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Xiong, Z., Wang, Z., Du, C., Zhu, R., Xiao, J., Lu, T. (2018). An Asian Face Dataset and How Race Influences Face Recognition. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11165. Springer, Cham. https://doi.org/10.1007/978-3-030-00767-6_35
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