Ideal Modified Adachi Chaotic Neural Networks and active shape model for infant facial cry detection on still image

In this paper, we develop a pattern recognition system to detect weather an infant is crying or not just by using his facial feature. The system must first detect the baby face by using the Haar-like feature, then find the facial component using trained active shape model (ASM). The extracted feature then fed to Chaotic Neural Network Classifier. We designed the system so that when the testing pattern is not a crying baby the system will be chaotic, but when the testing pattern is a crying baby face the system must switch to being periodic. Predicting whether a baby is crying based only on facial feature is still a challenging problem for existing computer vision system. Although crying baby can be detected easier using sound, most CCTV don´t have microphone to record the sound. This is the reason why we only use facial feature. Chaotic Neural Network (CNN) has been introduced for pattern recognition since 1989. But only recently that CNN receive a great attention from computer vision people. The CNN that we use in this paper is the Ideal Modified Adachi Neural Network (Ideal-M-AdNN). Experiments show that Ideal-M-AdNN with ASM feature able to detect crying baby face with accuracy up to 93%. But nevertheless this experiment is still novel and only limited to still image.