活泼
计算机科学
人工智能
计算机视觉
人脸检测
凝视
目标检测
探测器
对象类检测
眼动
生物识别
模式识别(心理学)
旋转(数学)
面子(社会学概念)
面部识别系统
电信
程序设计语言
社会科学
社会学
作者
Naseem Ahmad,Monsley K. Anish,Saharul Alom Barlaskar,Kuldeep Yadav,Rabul Hussain Laskar,Ashraf Hossain
标识
DOI:10.1109/tensymp54529.2022.9864431
摘要
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.
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