Classifying Childrens and Adults Faces by Bio-inspired Features
Children are usually treated differently from adults in many computer vision applications. To classify children from adults by face images in a natural and non-intrusive way, a method using improved bio-inspired features (C1-S) is presented in this paper. To reduce the negative influence of individual differences, active shape model (ASM) is used to extract 58 landmarks for face normalization. Motivated by quantitative model of visual cortex, we proposed C1-S features to represent each face. The features output from C1 units consider not only the points defined by grid size but also the points defined by ASM fitting results. By adding shape features, C1-S features have better performance in SVM classification. Experiment results show that our method provides good classification accuracy and can be used for home video surveillance and parental control.
active shape mode facial feature extraction facial normalization biological inspired features Gabor filter svm
Shaoyu Wang Xiaoling Xia Jiajin Le Songshao Yang Xiaoyong Liao
School of Computer Science and Technology Donghua University Shanghai, China Department of System Design Fukong Hualong Microsystem Technology Co.,LTD Shanghai, China
国际会议
厦门
英文
673-677
2010-10-29（万方平台首次上网日期，不代表论文的发表时间）