TY - GEN
T1 - An infant emotion recognition system using visual and audio information
AU - Fang, Chiung Yao
AU - Ma, Chung Wen
AU - Chiang, Meng Lin
AU - Chen, Sei Wang
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/5
Y1 - 2017/6/5
N2 - This study presents an infant emotion recognition system using visual and audio information for infants aged 1 to 7 months. The system is divided into two parts, image processing and speech processing. The image processing part detects the infant's face and extracts facial expressions features. In the face detection stage, the system selects the largest skin color region as the facial area, while in the facial expressions feature extraction stage, the system uses the local ternary pattern (LTP) technology to label facial contours and calculates their corresponding Zernike moments. In the speech processing part, the system uses common mel-frequency cepstral coefficients (MFCCs) and its delta cepstrum coefficients as vocalization features. Finally, the system uses support vector machines (SVMs) to classify the facial expressions features and vocalization features, respectively. By combining these types of classification results, the system reaches a decision about the infant's emotion. The average recognition rate of infant emotions is 85.3% in the experiments which, in our view, makes the proposed system robust and efficient.
AB - This study presents an infant emotion recognition system using visual and audio information for infants aged 1 to 7 months. The system is divided into two parts, image processing and speech processing. The image processing part detects the infant's face and extracts facial expressions features. In the face detection stage, the system selects the largest skin color region as the facial area, while in the facial expressions feature extraction stage, the system uses the local ternary pattern (LTP) technology to label facial contours and calculates their corresponding Zernike moments. In the speech processing part, the system uses common mel-frequency cepstral coefficients (MFCCs) and its delta cepstrum coefficients as vocalization features. Finally, the system uses support vector machines (SVMs) to classify the facial expressions features and vocalization features, respectively. By combining these types of classification results, the system reaches a decision about the infant's emotion. The average recognition rate of infant emotions is 85.3% in the experiments which, in our view, makes the proposed system robust and efficient.
KW - Zernike moments
KW - infant emotion recognition
KW - infant monitoring system
KW - local ternary pattern (LTP)
KW - mel-frequency cepstral coefficients (MFCCs)
UR - http://www.scopus.com/inward/record.url?scp=85021439723&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85021439723&partnerID=8YFLogxK
U2 - 10.1109/IEA.2017.7939223
DO - 10.1109/IEA.2017.7939223
M3 - Conference contribution
AN - SCOPUS:85021439723
T3 - 2017 4th International Conference on Industrial Engineering and Applications, ICIEA 2017
SP - 284
EP - 291
BT - 2017 4th International Conference on Industrial Engineering and Applications, ICIEA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Industrial Engineering and Applications, ICIEA 2017
Y2 - 21 April 2017 through 23 April 2017
ER -