Eye Location Algorithm Based on Wavelet Theory and High Discrimination Features

Eye location is the prerequisite for face recognition and face analysis. However, the accuracy of eye location is vulnerably affected by non-uniform illumination changes and noise. To solve this problem, this paper proposes an algorithm based on wavelet theory and high discrimination features for eye location. Firstly, face images undergo illumination normalization utilizing wavelet theory. Secondly, the LTP and LPQ features are extracted from the eye-candidate regions. and then the eye probability map is subsequently obtained from the calculation of the responding classification value of SVR for each eye-candidate point. Lastly, applied Gaussian fitting method to the eye probability map and the human eye is located accurately. Extensive experiments on CUM PIE, Yale B and AR Face Databases demonstrate that our method can effectively overcome the bad effects of non-uniform illumination changes and noise for the accurate eye location, and improve the robustness to changes in illumination and outperform the state-of-the-art approaches in terms of the accuracy of eye localization.