Nguyen Duy, Halupka David, Aarabi Parham, Sheikholeslami Ali
Department of Electrical and Computer Engineering, University of Toronto, ON, Canada.
IEEE Trans Syst Man Cybern B Cybern. 2006 Aug;36(4):902-12. doi: 10.1109/tsmcb.2005.862728.
This paper proposes a new technique for face detection and lip feature extraction. A real-time field-programmable gate array (FPGA) implementation of the two proposed techniques is also presented. Face detection is based on a naive Bayes classifier that classifies an edge-extracted representation of an image. Using edge representation significantly reduces the model's size to only 5184 B, which is 2417 times smaller than a comparable statistical modeling technique, while achieving an 86.6% correct detection rate under various lighting conditions. Lip feature extraction uses the contrast around the lip contour to extract the height and width of the mouth, metrics that are useful for speech filtering. The proposed FPGA system occupies only 15050 logic cells, or about six times less than a current comparable FPGA face detection system.
本文提出了一种用于面部检测和唇部特征提取的新技术。还介绍了所提出的两种技术的实时现场可编程门阵列（FPGA）实现。面部检测基于朴素贝叶斯分类器，该分类器对图像的边缘提取表示进行分类。使用边缘表示可将模型大小显著减小至仅5184字节，比类似的统计建模技术小2417倍，同时在各种光照条件下实现86.6%的正确检测率。唇部特征提取利用唇部轮廓周围的对比度来提取嘴巴的高度和宽度，这些指标对语音过滤很有用。所提出的FPGA系统仅占用15050个逻辑单元，比当前类似的FPGA面部检测系统少约六倍。
