Eye detection method using gray intensity information and support vector machines

This article introduces an efficient eye detection method based on gray intensity information and support vector machines (SVM). Firstly, using the evidence that gray intensity variation in the eye region is obvious, an eye variance filter (EVF) was constructed. Within the selected eye search region, the eye variance filter was used to find out eye candidate regions. Secondly, a trained support vector machine classifier was employed to detect the precise eye location among these eye candidate regions. Lastly, the eye center, i. e., iris center, could be located by the proposed gray intensity information rate. The proposed method was evaluated on the BioID, FERET, and IMM face databases, respectively. The correct rates of eye detection on face images without glasses are 98.2%, 97.8% and 98.9% respectively and that with glasses is 94.9%. The correct rates of eye center localization are 90.5%, 88.3% and 96.1%, respectively. Compared with state-of-the-art methods, the proposed method achieves good detection performance.