The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Detection Using the Hausdorff Distance
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Robust precise eye location under probabilistic framework
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Effective designation of specific shots on video service system utilizing Mahalanobis distance
IEEE Transactions on Consumer Electronics
A Bayesian discriminating features method for face detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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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.