Intelligent human eye state identification based on 2DPCA and skin color

Human eye state identification can be applied not only to monitoring of the drowsiness of a human car driver but also to medical treatment facilitating system for monitoring neonate or stuporous patient. Once the patient awake and open his eyes, human eye state identification system can notify nurses to take care of the patient. In this work, we propose an intelligent human eye state identification algorithm based on 2DPCA and skin color. Adaboost face detection function of OpenCV is first adopted to detect the human faces in color images acquired from camera. Then, we develop a more precise HSV skin color model and use it to eliminate the false alarms in the previous stage. Next, a heuristic segmentation method based on skin color and face geometry is proposed to segment the region of eyes, from which 2DPCA is then adopted to extract the features and identify the opening or closing state of eyes. We study three kinds of 2DPCA, i.e. 2DPCA, T-2DPCA and (2D) 2PCA, and compare their performance. Experimental results reveal that our algorithm can achieve over 90% accuracy rate.