Title :
Infant hungry recognition based on neural network and AR model
Author :
Mansor, M.N. ; Rejab, M.N. ; Syam, S.H.-F. ; Syam, A.H.-F.
Author_Institution :
Sch. of Mechatron. Eng., Univ. Malaysia Perlis, Kangar, Malaysia
Abstract :
To deal with nonverbal life was a difficult task. To study their behaviour without knowing what their needs is another crucial issue. A lot of researches have been rapidly investigated. Thus, in this paper we proudly proposed a system to determine the hungry infant based on their facial expression. A Haar Cascade face detection method was implemented. Autoregressive Model (AR) was employed for the coefficient extraction. Some other statistical methods were used as the feature extraction. Finally Neural network (NN) with 93.78% accuracy was accepted.
Keywords :
emotion recognition; face recognition; AR model; Haar Cascade face detection method; autoregressive model; facial expression; infant hungry recognition; neural network; nonverbal life; Educational institutions; Feature extraction; Mathematical model; Neural networks; Pediatrics; Training; Video recording; Autoregressive Model (AR); Detection of facial changes; NICU patient; Neural Network classifier;
Conference_Titel :
Instrumentation & Measurement, Sensor Network and Automation (IMSNA), 2012 International Symposium on
Conference_Location :
Sanya
Print_ISBN :
978-1-4673-2465-6
DOI :
10.1109/MSNA.2012.6324596