Abstract
Eye detection has been a critical problem for iris recognition, face recognition and some other applications. However, the unconstrained scene brings a lot of challenging problems to eye detection such as occlusion, rotation, blur and complex background etc. In this paper, we propose a novel eye detection algorithm for near-infrared image. We put forward four factors, which are IVSF, PLG, DRDF and IOSF to represent eye region features. The method is mainly composed of two steps. Firstly, candidate positions are generated. Secondly, a multi-strategy fusion method is designed to confirm final eye position. The experimental results demonstrate that the proposed algorithm is accurate and fast compared with some existing methods.
X. Zhang—Professor with the Department of Mathematics, College of Sciences, Northeastern University, Heping District, Shenyang, Liaoning Province, P.R. China.
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Acknowledgement
This research is supported by National Natural Science Funds of China, No. 61703088, the Doctoral Scientific Research Foundation of Liaoning Province, No. 20170520326 and “the Fundamental Research Funds for the Central Universities”, N160503003.
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Wang, Q., Lian, Y., Sun, T., Chu, Y., Zhang, X. (2018). Efficient Near-Infrared Eye Detection Utilizing Appearance Features. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_52
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