TY - GEN
T1 - Towards automated recognition of facial expressions in animal models
AU - Blumrosen, Gaddi
AU - Hawellek, David
AU - Pesaran, Bijan
N1 - Funding Information:
6. Acknowledgments We would like to thank, Marsela Rubiano, Breonna Ferrentino, and Nia Boles for their efforts in tagging the lower facial expression video clips, Eshkol Fund Mr. Avraham and Mrs. Rivka Blumrosen encouragement (GB), Leopoldina Fellowship Programme Grant (LPDS/LPDR 2012-09) (DH), and an Award from the Simons Collaboration on the Global Brain (BP).
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - Facial expressions play a significant role in the expression of emotional states, such as fear, surprise, and happiness in humans and other animals. The current systems for recognizing animal facial expression model in Non-human primates (NHPs) are currently limited to manual decoding of the facial muscles and observations, which is biased, time-consuming and requires a long training process and certification. The main objective of this work is to establish a computational framework for facial recognition systems for automatic recognition NHP facial expressions from standard video recordings with minimal assumptions. The suggested technology consists of: 1)a tailored facial image registration for NHPs; 2)a two-layers unsupervised clustering algorithm that forms an ordered dictionary of facial images for different facial segments; 3)extract dynamical temporal-spectral features;, and recognize dynamic facial expressions. The feasibility of the methods was verified using video recordings of an NHP under various behavioral conditions, recognizing typical NHP facial expressions in the wild. The results were compared to three human experts, and show an agreement of more than 82%. This work is the first attempt for efficient automatic recognition of facial expressions in NHPs using minimal assumptions about the physiology of facial expressions.
AB - Facial expressions play a significant role in the expression of emotional states, such as fear, surprise, and happiness in humans and other animals. The current systems for recognizing animal facial expression model in Non-human primates (NHPs) are currently limited to manual decoding of the facial muscles and observations, which is biased, time-consuming and requires a long training process and certification. The main objective of this work is to establish a computational framework for facial recognition systems for automatic recognition NHP facial expressions from standard video recordings with minimal assumptions. The suggested technology consists of: 1)a tailored facial image registration for NHPs; 2)a two-layers unsupervised clustering algorithm that forms an ordered dictionary of facial images for different facial segments; 3)extract dynamical temporal-spectral features;, and recognize dynamic facial expressions. The feasibility of the methods was verified using video recordings of an NHP under various behavioral conditions, recognizing typical NHP facial expressions in the wild. The results were compared to three human experts, and show an agreement of more than 82%. This work is the first attempt for efficient automatic recognition of facial expressions in NHPs using minimal assumptions about the physiology of facial expressions.
UR - http://www.scopus.com/inward/record.url?scp=85046272479&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046272479&partnerID=8YFLogxK
U2 - 10.1109/ICCVW.2017.332
DO - 10.1109/ICCVW.2017.332
M3 - Conference contribution
AN - SCOPUS:85046272479
T3 - Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
SP - 2810
EP - 2819
BT - Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017
Y2 - 22 October 2017 through 29 October 2017
ER -