TY - GEN
T1 - Ideal Modified Adachi Chaotic Neural Networks and active shape model for infant facial cry detection on still image
AU - Kristian, Yosi
AU - Hariadi, Mochamad
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/9/3
Y1 - 2014/9/3
N2 - 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.
AB - 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.
KW - Chaotic neural networks
KW - active shape model
KW - chaotic pattern recognition
KW - ideal modified adachi neural network
KW - infant facial cry detection
UR - http://www.scopus.com/inward/record.url?scp=84908474240&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2014.6889520
DO - 10.1109/IJCNN.2014.6889520
M3 - Conference contribution
AN - SCOPUS:84908474240
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 2783
EP - 2787
BT - Proceedings of the International Joint Conference on Neural Networks
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 International Joint Conference on Neural Networks, IJCNN 2014
Y2 - 6 July 2014 through 11 July 2014
ER -