Affiliations: [a] School of Life Sciences, Beijing Institute of Technology, Beijing 100081, China | [b] Intelligent Human-Machine Systems Lab, College of Engineering, Northeastern University, Boston, MA 02115, USA | [c] School of Automation, Beijing Institute of Technology, Beijing 100081, China
Correspondence:
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Corresponding author: Xiaoying Tang, School of Life Sciences, Beijing Institute of Technology, Detailed permanent address: 708 room, 5 building, 5 South Zhongguancun Street, Haidian District, Beijing 100081, China. Tel.: +86 13301318097; Fax: +86 010 68918820; E-mail: [email protected].
Abstract: Eye detection plays an important role in many fields, because eyes provide prominent facial feature information. However, changes in face pose, illumination variation, with glasses, and eye occlusions can make it difficult to detect eyes well from facial images. This paper proposes a hybrid model for eye detection. The model is an integration of two classifiers: Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). In order to improve the speed of detection in the system, an eye variance filter (EVF) is constructed for eliminating most of noneye images to keep less candidate eye images. The CNN then works as a trainable feature extractor to explicitly extract various latent eye features. Finally, the trained SVM classifier is employed for eye verification instead of using the CNN classification function. Experiments applying the model have been conducted on the BioID, IMM, FERET and ORL face databases. Comparisons with other methods on the same databases indicate that this hybrid model has achieved a higher detection accuracy. Extensive experiments demonstrate the robustness and efficiency of our method by testing it on different facial images with varying eye conditions.
