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http://dx.doi.org/10.15207/JKCS.2018.9.8.001

A Method of Eye and Lip Region Detection using Faster R-CNN in Face Image  

Lee, Jeong-Hwan (Department of Electronic Engineering, Andong University)
Publication Information
Journal of the Korea Convergence Society / v.9, no.8, 2018 , pp. 1-8 More about this Journal
Abstract
In the field of biometric security such as face and iris recognition, it is essential to extract facial features such as eyes and lips. In this paper, we have studied a method of detecting eye and lip region in face image using faster R-CNN. The faster R-CNN is an object detection method using deep running and is well known to have superior performance compared to the conventional feature-based method. In this paper, feature maps are extracted by applying convolution, linear rectification process, and max pooling process to facial images in order. The RPN(region proposal network) is learned using the feature map to detect the region proposal. Then, eye and lip detector are learned by using the region proposal and feature map. In order to examine the performance of the proposed method, we experimented with 800 face images of Korean men and women. We used 480 images for the learning phase and 320 images for the test one. Computer simulation showed that the average precision of eye and lip region detection for 50 epoch cases is 97.7% and 91.0%, respectively.
Keywords
Deep Learning; faster R-CNN; Eye and Lip Detection; Image Recognition;
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Times Cited By KSCI : 2  (Citation Analysis)
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