[论文解读] Neural Network Approach for Eye Detection

Vijaya Laxmi, P. Sudhakara Rao+1 more|arXiv (Cornell University)|May 23, 2012

Image and Object Detection Techniques参考文献 1被引用 9

一句话总结

该论文提出了一种基于神经网络的眼部检测系统，采用Gabor滤波器和SVM进行特征提取与降维，随后通过神经网络分类以区分眼部与非眼部区域。该方法在100张图像的数据集上，于不同光照、姿态和表情条件下实现了98%的检测成功率，展现出在实时驾驶员疲劳监测系统中的鲁棒性。

ABSTRACT

Driving support systems, such as car navigation systems are becoming common and they support driver in several aspects. Non-intrusive method of detecting Fatigue and drowsiness based on eye-blink count and eye directed instruction controlhelps the driver to prevent from collision caused by drowsy driving. Eye detection and tracking under various conditions such as illumination, background, face alignment and facial expression makes the problem complex.Neural Network based algorithm is proposed in this paper to detect the eyes efficiently. In the proposed algorithm, first the neural Network is trained to reject the non-eye regionbased on images with features of eyes and the images with features of non-eye using Gabor filter and Support Vector Machines to reduce the dimension and classify efficiently. In the algorithm, first the face is segmented using L*a*btransform color space, then eyes are detected using HSV and Neural Network approach. The algorithm is tested on nearly 100 images of different persons under different conditions and the results are satisfactory with success rate of 98%.The Neural Network is trained with 50 non-eye images and 50 eye images with different angles using Gabor filter. This paper is a part of research work on "Development of Non-Intrusive system for real-time Monitoring and Prediction of Driver Fatigue and drowsiness" project sponsored by Department of Science & Technology, Govt. of India, New Delhi at Vignan Institute of Technology and Sciences, Vignan Hills, Hyderabad.

研究动机与目标

• 开发一种非侵入式、实时的驾驶员疲劳与困倦监测系统，基于眼部检测技术。
• 在光照变化、背景复杂、面部对齐不一致及表情多变的条件下，提升眼部检测的准确性。
• 将神经网络与Gabor滤波及SVM结合，实现高效特征提取与分类。
• 在真实世界条件下，基于包含100张图像的多样化数据集验证该系统性能。
• 支持通过非侵入式生物特征监测技术，推动智能驾驶辅助系统的发展。

提出的方法

• 利用L*a*b*颜色空间从图像中分割出人脸区域，以隔离面部区域。
• 通过HSV颜色空间进行初始基于颜色的滤波，检测眼部区域。
• 应用Gabor滤波器从眼部与非眼部区域提取纹理特征，以增强判别能力。
• 使用支持向量机（SVM）进行特征维数压缩，提升分类效率。
• 在50张眼部图像与50张非眼部图像（包含不同角度）上训练神经网络，以分类区域为眼部或非眼部。
• 最终检测流程结合颜色分割、特征提取与神经网络分类，实现鲁棒的眼部定位。

实验结果

研究问题

• RQ1 混合使用神经网络与Gabor滤波的方法是否能在光照与面部条件变化下实现高精度的眼部检测？
• RQ2 基于SVM的维数压缩与神经网络分类相结合，在区分眼部与非眼部区域方面效果如何？
• RQ3 该系统在真实场景中，对不同面部姿态与表情的眼部检测性能如何？
• RQ4 所提出的方法能否支持高可靠性的实时驾驶员疲劳监测？
• RQ5 L*a*b*与HSV颜色空间的结合如何提升初始人脸与眼部区域分割的准确性？

主要发现

• 所提系统在100张测试图像中，于不同条件下实现了98%的检测成功率。
• 使用Gabor滤波器显著提升了基于纹理的特征提取能力，增强了眼部区域的判别性。
• 基于SVM的维数压缩提升了神经网络的分类速度与准确性。
• 神经网络在包含50张眼部与50张非眼部图像、且角度多样的平衡数据集上成功训练。
• L*a*b*与HSV颜色空间的结合提升了初始人脸与眼部区域定位的准确性。
• 该方法对光照变化、背景差异及面部表情变化表现出强鲁棒性。