Abstract
Eye detection is a preliminary yet important step for face recognition and analysis. It is a challenging problem especially for unconstrained images. We propose a coarse-to-fine eye detection approach by using a two-level convolutional neural network which follows a biologically-inspired trainable architecture. The first level of our network roughly detects initial bounding boxes, whereas the second level judges whether the detected bounding boxes belong to eyes or not and deletes the non-eye bounding boxes. All remaining bounding boxes yielded from the two-level network are finally merged to give the accurate locations of detected eyes. Experimental results demonstrate the effectiveness of our method for eye detection under complex scenarios.
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Ye, L., Zhu, M., Xia, S., Pan, H. (2014). Cascaded Convolutional Neural Network for Eye Detection Under Complex Scenarios. In: Sun, Z., Shan, S., Sang, H., Zhou, J., Wang, Y., Yuan, W. (eds) Biometric Recognition. CCBR 2014. Lecture Notes in Computer Science, vol 8833. Springer, Cham. https://doi.org/10.1007/978-3-319-12484-1_54
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DOI: https://doi.org/10.1007/978-3-319-12484-1_54
Publisher Name: Springer, Cham
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