Abstract
The human eye detection plays an important role in computer vision. Along with face detection, it is widely applied in practical security, surveillance, and warning systems such as eye tracking system, eye disease detection, gaze detection, eye blink, and drowsiness detection system. There have been many studies to detect eyes from applying traditional methods to using modern methods based on machine learning and deep learning. This network is deployed with two main blocks, namely the feature extraction block and the detection block. The feature extraction block starts with the use of the convolution layers, C.ReLU (Concatenated Rectified Linear Unit) module, and max pooling layers alternately, followed by the last six inception modules and four convolution layers. The detection block is constructed by two sibling convolution layers using for classification and regression. The experiment was trained and tested on CEW (Closed Eyes In The Wild), BioID Face and GI4E (Gaze Interaction for Everybody) dataset with the results achieved 96.48%, 99.58%, and 75.52% of AP (Average Precision), respectively. The speed was tested in real-time by 37.65 fps (frames per second) on Intel Core I7-4770 CPU @ 3.40 GHz.
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This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government. (MSIT) (2020R1A2C2008972)
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Nguyen, DL., Putro, M.D., Jo, KH. (2020). Human Eye Detector with Light-Weight and Efficient Convolutional Neural Network. In: Hernes, M., Wojtkiewicz, K., Szczerbicki, E. (eds) Advances in Computational Collective Intelligence. ICCCI 2020. Communications in Computer and Information Science, vol 1287. Springer, Cham. https://doi.org/10.1007/978-3-030-63119-2_16
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