Image Context-Driven Eye Location Using the Hybrid Network of k-Means and RBF
In this paper, we present a novel eye location approach based on image context analysis. It is robust from the image variations such as illumination, glasses frame, and eyebrows. Image context of an image is any observable relevant attributes with other images. Image context analysis is carried out using the hybrid network of k-means and RBF. The proposed eye location employs context-driven adaptive Bayesian framework to relive the effect due to uneven face images. The appearance of eye patterns is represented by Haar wavelet. It also employs a merging and arbitration strategy in order to manage the variations in illumination and geometrical characteristics of ambient eye regions due to glasses frames, eye brows, and so on. The located eye candidates are merged or eliminated, and adaptive arbitration strategy is used based on a minimizing energy function by probabilistic forces and image forces. The adaptation is carried out by the analysis of image context. The experimental results show that the proposed approach can achieve superior performance using various data sets to previously proposed methods.
KeywordsFace Image False Detection Bayesian Classifier Hybrid Network Probabilistic Force
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- 2.Haro, A., Flickner, M., Essa, I.: Detecting and tracking eyes by usingtheir physiological properties, dynamics, and appearance. In: IEEE International Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 163–168 (2000)Google Scholar
- 5.Zhou, H., Geng, X.: Projection functions for eye detection. Pattern Recognition (in press, 2004)Google Scholar
- 6.Ma, Y., Ding, X., Wang, Z., Wang, N.: Robust precise eye location under probabilistic framework. In: IEEE International Conference on Automatic Face and Gesture Recognition (2004)Google Scholar
- 7.Martinez, A.M.: Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Transactions on PAMI 24(6), 748–763 (2002)Google Scholar
- 8.Watabe, A., Komiya, K., Usuki, J., Suzuki, K., Ikeda, H.: Effective Designation of Specific Shots on Video Service System Utilizing Mahalanobis Distance. IEEE Transactions on Consumer Electronics 51(1) (February 2005)Google Scholar