Automated human behavioral analysis framework using facial feature extraction and machine learning

Emotional intelligence is essential in understanding and predicting human behavior. Although human emotion is best captured using non-intrusive methods, due to factors such as system complexity, computation time and decision response time, the reality of automated behavioral analysis is hindered. In this paper, we propose a framework capable of recognizing emotions of an individual to identify any suspicious behavior. Our research shows 91.1% of emotion classification accuracy for cooperative individuals using facial feature extraction and machine learning techniques, thus outperforming existing state-of-the-art approaches.

[1]  Ioannis Pitas,et al.  Facial feature extraction and pose determination , 2000, Pattern Recognit..

[2]  Takeo Kanade,et al.  The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[3]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[4]  Lisa Ann Osadciw,et al.  Resource optimization in distributed biometric recognition using wireless sensor network , 2009, Multidimens. Syst. Signal Process..

[5]  Timothy F. Cootes,et al.  Active Appearance Models , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  J. Reid,et al.  Is low Emotional Intelligence a primary causal factor in drug and alcohol addiction , 2009 .

[7]  Douglas A. Reynolds.  Gaussian Mixture Models , 2009, Encyclopedia of Biometrics.

[8]  N. Tsapatsoulis,et al.  Comparing Template-based , Feature-based and Supervised Classification of Facial Expressions from Static Images , 1999 .

[9]  Yang Song,et al.  IKNN: Informative K-Nearest Neighbor Pattern Classification , 2007, PKDD.

[10]  Paul A. Viola,et al.  Robust Real-time Object Detection , 2001 .

[11]  B. Scholkopf,et al.  Fisher discriminant analysis with kernels , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[12]  Ravi P. Ramachandran.  The Use of Pitch Prediction in Speech Coding , 1995 .

[13]  Jing Cai,et al.  The Research on Emotion Recognition from ECG Signal , 2009, 2009 International Conference on Information Technology and Computer Science.

[14]  Vinod Vaikuntanathan,et al.  Can homomorphic encryption be practical? , 2011, CCSW '11.

[15]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[16]  Takeo Kanade,et al.  Comprehensive database for facial expression analysis , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[17]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[18]  Lisa Ann Osadciw,et al.  Secure Health Monitoring Network against Denial-Of-Service Attacks Using Cognitive Intelligence , 2008, 6th Annual Communication Networks and Services Research Conference (cnsr 2008).

[19]  Wendi B. Heinzelman,et al.  Cloud-Vision: Real-time face recognition using a mobile-cloudlet-cloud acceleration architecture , 2012, 2012 IEEE Symposium on Computers and Communications (ISCC).

[20]  Lisa Ann Osadciw,et al.  Sensor Communication Network Using Swarm Intelligence , 2002 .