Various Techniques Involved in Facial Expression Recognition-Review
Facial expressions transfers non-verbal clues, which play an imperative role in interpersonal relations. Automatic appreciation of facial expressions can be a central component of ordinary human-machine interfaces; it may also be used in communication science and in scientific practice. Although humans recognize facial expressions nearly without effort or delay, reliable expression recognition by appliance is still a challenge. A system that performs these operations more accurately and in real time would be crucial to achieve a human like interaction between man and machine. This paper presents a high-level outline of unconscious expression recognition; it highlights the key system components and some research contests. This paper reviews the past work done in solving these problems for image sequences and a number of methodological approaches relative to facial expression recognition systems and proposes further research areas that require more attention towards the successful implementation of a more efficient channel for machine-emotion interaction.
C. Shan, S. Gong, and P. W. McOwan, “Facial ExpressionRecognitionbased on Local Binary Patterns: A Comprehensive Study,” Image andVision Computing, vol. 27, no. 6, pp. 803-816, May 2009.
Y. Tian, L. Brown, A. Hampapur, S. Pankanti, A. Senior, and R. Bolle,“Real World Real-Time Automatic Recognition of Facial Expressions,”in Proc. IEEE Workshop on Performance Evaluation of Tracking andSurveillance, USA, pp. 9-16, 2003
P. Ekman, W. V. Friesen, and P. Ellsworth. Emotion in theHuman Face: Guidelines for Research and an Integration of Findings.Pergamon Press, New York, 1972.
P. Ekman and W. V. Friesen. Unmasking the Face: A Guide toRecognizing Emotions from Facial Clues. Prentice Hall, EnglewoodCli_s, New Jersey, 1975
Ekman. Telling Lies: Clues to Deceit in the Marketplace, Politics,and Marriage. W. W. Norton & Company, New York, 3rd edition,2001
R. Axelrod. The Evolution of Cooperation. Basic Books, September 1985.
XIE Liping et.al, “Video-based Facial Expression Recognition Using Histogram Sequence of Local Gabor Binary Patterns from Three Orthogonal Planes”, 33rd Chinese Control Conference, 2014.
Farhan Bashar et.al, “Expression Recognition Based on Median Ternary Pattern (MTP)”, International Conference on Electrical Information and Communication Technology, 2013.
Muzammil Abdurrahman, et.al, “Gabor Wavelet Transform Based Facial Expression Recognition Using PCA and LBP”, IEEE 22nd Signal Processing and Communications Applications Conference, 2014.
Byungsung Lee et al, “Classification of Facial Expression Using SVM for Emotion Care Service” , Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing.
Huang, M. W et.al, “A Novel Method of Facial Expression Recognition Based on GPLVM plus SVM”, ICSP, 2010.
Kenz Ahmed Bozed et.al, “Detection of Facial Expressions based on Morphological Face Features and Minimum Distance Classifier”, 14th international conference on Sciences and Techniques of Automatic control & computer engineering, 2013.
Ahmad Poursaberi et.al, “An Efficient Facial Expression Recognition System in Infrared Images”, Fourth International Conference on Emerging Security Technologies, 2013.
Tanvi Sheikh et.al, “Enhanced K-means Based Facial Expression Recognition System”, International Journal of Advances In Computer Science and Cloud Computing, 2013.
Shan He et.al, “Spontaneous facial expression recognition based on feature point tracking”, Sixth International Conference on Image and Graphics, 2011.
Nisha Thomas et.al, “Facial Expression Recognition System using Neural Network and MATLAB”.
Fengjun Chen et.al,” Research on a Method of Facial Expression Recognition”, ICEMI, 2009.
Yoshihiro Miyakoshi et.al, “Facial Emotion Detection Considering Partial Occlusion of Face Using Bayesian Network”.
WenmingZheng et.al, “Multi-view Facial Expression Recognition Based on Group Sparse Reduced-rank Regression”, IEEE Transactions on Affective Computing.
Thitipan Wannakijmongkol et.al, “An Improved Adaptive Discriminant Analysis for Single Sample Face Recognition”, 11th International Joint Conference on Computer Science and Software Engineering JCSSE, 2014.
Kai Li et.al, “A Data-Driven Approach for Facial expression Retargeting in Video”, IEEE TRANSACTIONS ON MULTIMEDIA, 2014.
Jinkuang Cheng et.al, “A Facial Expression Based Continuous Emotional State Monitoring System with GPU Acceleration”.
Chi-Ting Hsu1 et.al, “Facial Expression Recognition using Hough Forest”.
Jun Wang et.al, “3D Facial Expression Recognition Based on Primitive Surface Feature Distribution”.
Ruo Du1 et.al, “Facial Expression Recognition Using Histogram Variances Faces”, 2009.
NazilPerveen et.al, “Facial Expression Recognition Using Facial Characteristic Points and Gini Index”.
Kai-Tai Song et.al, “Facial Expression Recognition Based on Mixtureof Basic Expressions and Intensities”, IEEE International Conference on Systems, Man, and Cybernetics, 2012.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.