Eye detection using Faster-RCNN

Eye detection is essential in many computer vision applications such as driver drowsiness detection, human behavior analysis, liveness detection, gaze estimation, etc. However, eye detection in a facial image is challenging due to face rotation, pose variation, scale variation, occlusion, etc. Therefore, Faster RCNN deep learning-based eye detection model is proposed under the occlusion and pose variation. Pre-processing was done using contrast stretching which makes the database better for detection. Fine-tuning of hyperparameters and augmentation makes the detector more accurate and robust. The effectiveness of the eye detection model was analyzed on the publicly available AR and GI4E databases. This model gives 98.32% and 98.11% accuracy for the AR and GI4E databases with a 0.52 ms/image computational time. Extensive experiments suggested that our proposed model resulted better than state-of-the-art methods for eye detection.