TY - GEN
T1 - Subpixel subtle motion estimation of micro-expressions multiclass classification
AU - Muna, Niyalatul
AU - Rosiani, Ulla Delfana
AU - Yuniarno, Eko Mulyanto
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/29
Y1 - 2017/11/29
N2 - Communication between one individual and another can be delivered in different ways, not only through speech, writing and body motions. Communication can also be delivered through basic emotions one feels through visible facial expressions. Facial expressions displayed in the forms of emotional expressions can be demonstrated briefly or quickly, which is known as micro-expression. The subtle motion of micro facial expression in every exchange makes many people find it difficult to identify and recognize the ongoing emotion. Therefore, this study proposes the recognition of micro-expression emotions with estimated subtle motion in the image sequence based on onset-frame, apex-frame, and offset-frame for feature extraction. The method used for feature extraction are the combination of Block Matching Algorithm with Taylor Series Approximation referred to as Subpixel Subtle Motion Estimation (SME). Multiclass Classification process using Multilayer Perceptron (MLP) Backpropagation and Support Vector Machine (SVM) as a comparison. The evaluation results show the best accuracy 85.07%, with the Mean Absolute Error 0.0597 and Root Mean Square Error 0.2443.
AB - Communication between one individual and another can be delivered in different ways, not only through speech, writing and body motions. Communication can also be delivered through basic emotions one feels through visible facial expressions. Facial expressions displayed in the forms of emotional expressions can be demonstrated briefly or quickly, which is known as micro-expression. The subtle motion of micro facial expression in every exchange makes many people find it difficult to identify and recognize the ongoing emotion. Therefore, this study proposes the recognition of micro-expression emotions with estimated subtle motion in the image sequence based on onset-frame, apex-frame, and offset-frame for feature extraction. The method used for feature extraction are the combination of Block Matching Algorithm with Taylor Series Approximation referred to as Subpixel Subtle Motion Estimation (SME). Multiclass Classification process using Multilayer Perceptron (MLP) Backpropagation and Support Vector Machine (SVM) as a comparison. The evaluation results show the best accuracy 85.07%, with the Mean Absolute Error 0.0597 and Root Mean Square Error 0.2443.
KW - Block matching algorithm
KW - Component
KW - Micro-expressions
KW - Multiclass classification
KW - Subpixel
KW - Subtle motion estimation
KW - Taylor series approximation
UR - http://www.scopus.com/inward/record.url?scp=85043481964&partnerID=8YFLogxK
U2 - 10.1109/SIPROCESS.2017.8124558
DO - 10.1109/SIPROCESS.2017.8124558
M3 - Conference contribution
AN - SCOPUS:85043481964
T3 - 2017 IEEE 2nd International Conference on Signal and Image Processing, ICSIP 2017
SP - 325
EP - 330
BT - 2017 IEEE 2nd International Conference on Signal and Image Processing, ICSIP 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Conference on Signal and Image Processing, ICSIP 2017
Y2 - 4 August 2017 through 6 August 2017
ER -