Classifying children´s 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.