1000 Fps Highly Accurate Eye Detection with Stacked Denoising Autoencoder

Wei Tang, Yongzhen Huang, Liang Wang

Abstract: Eye detection is an important step for a range of applications such as iris and face recognition. For eye detection in practice, speed is as equally important as accuracy. In this paper, we propose a super-fast (1000 fps on a general PC) eye detection method based on the label map of the raw image without face detection. We firstly produce the label map of a raw image according to the coordinates of its bounding box . Then we train a stacked denoising autoencoder (SDAE) which is specifically designed to learn the mapping from the raw image to the label map. Finally, through an effective post-processing step, we obtain the bounding boxes of two eyes. Experimental results show that our method is about 2,500 times faster than the deformable part-based model (DPM) while maintaining a comparable accuracy. Also, our method is much better than the popular LBP+Cascade model in terms of both accuracy and speed.