Finding a Proper Approach to Obtain Cognitive Parameters from Human Faces under Illumination Variations

Extract and recognize face features can become a difficult problem, especially in environments with dynamic illumination conditions. For example, changing faces position respect to the camera and varying intensity of the light source, among others. Trying to mitigate illumination variation effects have been studied using different approaches, but a comparison between them and their characteristics such as processing times using a classifier is still needed. This is important to try to find a properly algorithm that can fulfill the demanding requirements for some cognitive applications. In this work, an illumination invariant face feature recognition using dual-Tree complex wavelet transform is presented. A validation and testing of the proposed approach is performed using Yale B faces dataset, showing that we can obtain 90.7% to 98.5% recognition rates on the dataset depending of the illumination level with the proposed method. Additionally, a comparison between 21 other illumination normalization methods using the same classification approach is presented. Finally, an online algorithm is implemented and tested on real environments under varying lighting conditions, which is capable to recognize subject faces, and their eyes and mouth status. In particular, the on-line results of the proposed approach show recognition rates for eye blink detection from 84.1% to 90.4% on 55[ms], which may be useful for time demanding applications such as sleepiness detection.