TY - GEN
T1 - Combining temporal interpolation and DCNN for faster recognition of micro-expressions in video sequences
AU - Mayya, Veena
AU - Pai, Radhika M.
AU - Pai, M. M.Manohara
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/2
Y1 - 2016/11/2
N2 - Micro-expressions are the hidden human emotions that are short lived and are very hard to detect them in real time conversations. Micro-expressions recognition has proven to be an important behavior source for lie detection during crime interrogation. SMIC and CASME II are the two widely used, spontaneous micro-expressions datasets which are available publicly with baseline results that uses LBP-TOP for feature extraction. Estimation of correct parameters is the key factor for feature extraction using LBP-TOP, which results in long computation time. In this paper, the video sequences are interpolated using temporal interpolation(TIM) and then the facial features are extracted using deep convolutional neural network(DCNN) on CUDA enabled General Purpose Graphics Processing Unit(GPGPU) system. Results show that the proposed combination of DCNN and TIM can achieve better performance than the results published in baseline publications. The feature extraction time is reduced due to the usage of GPU enabled systems.
AB - Micro-expressions are the hidden human emotions that are short lived and are very hard to detect them in real time conversations. Micro-expressions recognition has proven to be an important behavior source for lie detection during crime interrogation. SMIC and CASME II are the two widely used, spontaneous micro-expressions datasets which are available publicly with baseline results that uses LBP-TOP for feature extraction. Estimation of correct parameters is the key factor for feature extraction using LBP-TOP, which results in long computation time. In this paper, the video sequences are interpolated using temporal interpolation(TIM) and then the facial features are extracted using deep convolutional neural network(DCNN) on CUDA enabled General Purpose Graphics Processing Unit(GPGPU) system. Results show that the proposed combination of DCNN and TIM can achieve better performance than the results published in baseline publications. The feature extraction time is reduced due to the usage of GPU enabled systems.
UR - https://www.scopus.com/pages/publications/85007359830
UR - https://www.scopus.com/inward/citedby.url?scp=85007359830&partnerID=8YFLogxK
U2 - 10.1109/ICACCI.2016.7732128
DO - 10.1109/ICACCI.2016.7732128
M3 - Conference contribution
AN - SCOPUS:85007359830
T3 - 2016 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016
SP - 699
EP - 703
BT - 2016 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016
A2 - Rodrigues, Joel J. P. C.
A2 - Siarry, Patrick
A2 - Perez, Gregorio Martinez
A2 - Tomar, Raghuvir
A2 - Pathan, Al-Sakib Khan
A2 - Mehta, Sameep
A2 - Thampi, Sabu M.
A2 - Berretti, Stefano
A2 - Gorthi, Ravi Prakash
A2 - Pathan, Al-Sakib Khan
A2 - Wu, Jinsong
A2 - Li, Jie
A2 - Jain, Vivek
A2 - Rodrigues, Joel J. P. C.
A2 - Atiquzzaman, Mohammed
A2 - Rodrigues, Joel J. P. C.
A2 - Bedi, Punam
A2 - Kammoun, Mohamed Habib
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016
Y2 - 21 September 2016 through 24 September 2016
ER -
