TY - GEN
T1 - Recognition between smiling and neutral facial display with power LBP operator
AU - Nurzynska, Karolina
AU - Smolka, Bogdan
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/10/30
Y1 - 2015/10/30
N2 - Automatic recognition of emotions can be helpful in many situations. Therefore, research towards providing tools enabling the most accurate recognition is necessary. In this work, the problem of smiling and neutral facial display classification is addressed. For image description, the local binary patterns (LBP) as well as the novel power LBP (PLBP) texture operators are exploited. Their performance is compared on several databases and examined for variations from the standard designs, such as uniform and rotation invariant approaches. The classification is performed with the use of support vector machine, SVM. The obtained results show that when applying detailed image division schema, which is characterised by many small image patches analyzed separately, the introduced PLBP overcomes the LBP method. Moreover, the uniform or rotation invariant LBP versions do not improve the classification. However, the accuracy achieved using different types of PLBP is higher than when applying the standard LBP technique.
AB - Automatic recognition of emotions can be helpful in many situations. Therefore, research towards providing tools enabling the most accurate recognition is necessary. In this work, the problem of smiling and neutral facial display classification is addressed. For image description, the local binary patterns (LBP) as well as the novel power LBP (PLBP) texture operators are exploited. Their performance is compared on several databases and examined for variations from the standard designs, such as uniform and rotation invariant approaches. The classification is performed with the use of support vector machine, SVM. The obtained results show that when applying detailed image division schema, which is characterised by many small image patches analyzed separately, the introduced PLBP overcomes the LBP method. Moreover, the uniform or rotation invariant LBP versions do not improve the classification. However, the accuracy achieved using different types of PLBP is higher than when applying the standard LBP technique.
KW - classification
KW - emotion recognition
KW - local binary pattern
KW - power LBP
UR - https://www.scopus.com/pages/publications/84961700481
U2 - 10.1109/EUROCON.2015.7313691
DO - 10.1109/EUROCON.2015.7313691
M3 - Conference contribution
AN - SCOPUS:84961700481
T3 - Proceedings - EUROCON 2015
BT - Proceedings - EUROCON 2015
A2 - Grana, Manuel
A2 - Corchado, Emilio
A2 - Fraile-Ardanuy, Jesus
A2 - Quintian, Hector
A2 - Kakarountas, Athanasios
A2 - Haase, Jan
A2 - Debono, Carl James
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - International Conference on Computer as a Tool, IEEE EUROCON 2015
Y2 - 8 September 2015 through 11 September 2015
ER -