Eye localization from thermal infrared images - Wang, Shangfei; Liu, Zhilei; Shen, Peijia; Ji, Qiang (Pattern recognition, 2013)
By using the knowledge of facial structure and temperature distribution, this paper proposes an automatic eye localization method from long wave infrared thermal images both with eyeglasses and without eyeglasses. First, with the help of support vector machine classifier, three gray-projection features are defined to determine whether a subject is with eyeglasses. For subjects with eyeglasses, the locations of valleys in the projection curve are used to perform eye localization. For subjects without eyeglasses, a facial structure consisting of 15 sub-regions is proposed to extract Haar-like features. Eight classifiers are learned from the features selected by Adaboost algorithm for left and right eye, respectively. A vote strategy is employed to find the most likely eyes. To evaluate the effectiveness of our approach, experiments are performed on NVIE and Equinox databases. The eyeglass detection results on NVIE database and Equinox database are 99.36% and 95%, respectively, which demonstrate the effectiveness and robustness of our eyeglass detection method. Eye localization results of within-database experiments and cross-database experiments on these two databases are very comparable with the previous results in this field, verifying the effectiveness and the generalization ability of our approach. •The eyes localization on samples both with glasses and without glasses are conducted simultaneously.•Three parameters capturing the temperature character caused by glasses are proposed for glasses detection.•The 15 sub-regions structure capturing the temperature distribution around the eyes is proposed for eyes localization.•The results of eyes localization conducted on samples without eyeglasses outperform those of research.•Eyes detection method is evaluated through a within experiment and a cross-corpus experiment.