TY - GEN
T1 - The Facial Stress Recognition Based on Multi-histogram Features and Convolutional Neural Network
AU - Prasetio, Barlian Henryranu
AU - Tamura, Hiroki
AU - Tanno, Koichi
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - The health disorders due to stress and depression should not be considered trivial because it has a negative impact on health. Prolonged stress not only triggers mental fatigue but also affects physical health. Therefore, we must be able to identify stress early. In this paper, we proposed the new methods for stress Recognition on three classes (neutral, low stress, high stress) from a facial frontal image. Each image divided into three parts, i.e. pairs of eyes, nose, and mouth. Facial features have extracted on each image pixel using DoG, HOG, and DWT. The strength of orthonormality features is considered by the RICA. The GDA distributes the nonlinear covariance. Furthermore, the histogram features of the image parts are applied at a depth-based learning of ConvNet to model the facial stress expression. The proposed method is used FERET databases for training and validation. The k-fold validation method is used as a validation with k=5. Based on the experiments result, the proposed method accuracy showing outperforms compared with other works.
AB - The health disorders due to stress and depression should not be considered trivial because it has a negative impact on health. Prolonged stress not only triggers mental fatigue but also affects physical health. Therefore, we must be able to identify stress early. In this paper, we proposed the new methods for stress Recognition on three classes (neutral, low stress, high stress) from a facial frontal image. Each image divided into three parts, i.e. pairs of eyes, nose, and mouth. Facial features have extracted on each image pixel using DoG, HOG, and DWT. The strength of orthonormality features is considered by the RICA. The GDA distributes the nonlinear covariance. Furthermore, the histogram features of the image parts are applied at a depth-based learning of ConvNet to model the facial stress expression. The proposed method is used FERET databases for training and validation. The k-fold validation method is used as a validation with k=5. Based on the experiments result, the proposed method accuracy showing outperforms compared with other works.
KW - ConvNet
KW - DoG
KW - DWT
KW - facial stress
KW - GDA
KW - HOG
KW - RICA
UR - https://www.scopus.com/pages/publications/85062206484
U2 - 10.1109/SMC.2018.00157
DO - 10.1109/SMC.2018.00157
M3 - Conference contribution
AN - SCOPUS:85062206484
T3 - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
SP - 881
EP - 887
BT - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Y2 - 7 October 2018 through 10 October 2018
ER -