Eye states detection by boosting Local Binary Pattern Histogram features

In this paper, we propose a novel method for eye states detection. The detection of eye states is treated as an appearance based binary classification problem. The whole eye region is first scanned by a series of blocks with various locations and scales. The Local Binary Pattern Histogram (LBPH) is then extracted from each block to form a descriptor of the local texture. A reference template for each block is later calculated as the optimal histogram which makes the distances between it and the LBPHs of different clusters most separable. For all the blocks, the bin-wise distances between local LBPHs and the corresponding reference templates are then extracted to form a feature set for classification. A cascaded AdaBoost learning is further employed to select most discriminative features from the whole feature set and to accelerate the classification. Experimental results demonstrate that our eye states detection algorithm and can give correct and robust detection results in real-time.