Artificial Intelligent Systems and Machine Learning

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

A Development of Emotion Recognition System


Affiliations
1 Gyan Ganga Institute of Technology and Sciences, Jabalpur (M.P.), India
     

   Subscribe/Renew Journal


Human Computer Interaction (HCI) is one of most interesting topic in machine visualization and image processing fields. Emotion recognition plays an important role in security and interpersonal communication. Biometric system helps in identification, security and authentication using face image. Recognize emotion of a person from occluded face image is a challenging task in emotion recognition. Feature are calculated for face image using Principe Component Analysis (PCA) and Two-Directional Two Dimension Principal Component Analysis [(2D) 2PCA] along with discrete wavelet transform. K-Nearest Neighbor (K-NN) and multiclass support vector machine used for classification of different emotion. This paper shows the comparative study of feature extraction and classification method. This study is performed in three dataset. JAFFE, CMU and CK database is used for calculating the classification rate of emotion recognition system .Resulting successful classification rate for JAFFE database is 91.8919% for CMU dataset classification rate is 70.339 % and for CK database resulting classification rate is 75.3073% using Multi class support vector machine. Multiclass support vector machine gives better result as compare to K-nearest neighbor.


Keywords

(2D) 2PCA (Two-Directional Two Dimension Principal Component Analysis), Principe Component Analysis (PCA), Multi Class Support Vector Machine (MSVM), K-Nearest Neighbor, JAFFE, CMU, CK Database.
User
Subscription Login to verify subscription
Notifications
Font Size

  • Vinícius Silva, Filomena Soares, João S. Esteves,Joana Figueiredo, Celina P. Leão, Cristina Santos, Ana PaulaPereira Real-time Emotions Recognition System. 2016 8th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT).

  • Tasnim Tarannum, Anwesha Pauly and Kamrul Hasan Talukder Human Expression Recognition Based on Facial Features 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV) .

  • I. Kotsia and I. Pitas, “Facial expression recognition in image sequences using geometric deformation features and support vector machines,” IEEE Trans. Image Process., vol. 16, no. 1, pp. 172–187, 2007.

  • P. Michel and R. El Kaliouby, “Real time facial expression recognition in video using support vector machines,” Proc. 5th Int. Conf. Multimodal interfaces - ICMI ’03, p. 258, 2003.

  • A. Garg, and V. Choudhary, Facial Expression Recognition using PrincipleComponent Analysis, (IJSRET) International Journal of Scientific Research Engineering and Technology, Vol. 1, No. 2, July 2012.

  • M. Kumbhar, M. Patil and A. Jadhav, Facial Expression Recognitionusing Gabor Wavelet, International Journal of Computer Applications (0975-8887), Vol. 68, No. 23, April 2013.

  • Deepjoy Das, Alok Chakrabarty Emotion Recognition from Face Dataset Using Deep Neural Nets Department of Computer Science &Engineering National Institute of Technology Meghalaya Meghalaya, India.

  • M. Shamim Hossain1, (Senior Member, IEEE), and Ghulam Muhammad2, (Member, IEEE) Special Section On Emotion-Aware Mobile Computing Digital Object Identifier 10.1109/ACCESS.2017.2672829

  • Humaid Alshamsi, Hongying Meng, Maozhen LiReal Time Facial Expression Recognition App Development on Mobile Phones 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD).

  • Austin Nicolai and Anthony Choi Facial Emotion Recognition 2015 IEEE International Conference on Systems, Man, and Cybernetics Using Fuzzy Systems.

  • Fatima Zahra SALMAM, Abdellah MADANI, Mohamed KISSI 13th International Conference Computer Graphics, Imaging and Visualization 2016.

  • N. Perveen, S. Gupta, and K. Verma, “Facial expression recognition using facial characteristic points and Gini index,” Engineering and Systems (SCES), Students Conference on. IEEE, 2012.

  • A. Saeed, A. Al-Hamadi, R. Niese, and M. Elzobi, “Effective geometric features for human emotion recognition,” Signal Processing (ICSP), 2012 IEEE 11th International Conference on, Vol. 1, pp. pp. 623-627, IEEE, 2012.

  • Y. Yacoob and L. Davis, “Computing spatio-temporal representations of human faces,” in Computer Vision and Pattern Recognition, 1994. Proceedings CVPR’94. 1994 IEEE Computer Society Conference On. IEEE, 1994, pp. 70–75.

  • M. J. Black and Y. Yacoob, “Tracking and recognizing rigid and non-rigid facial motions using local parametric models of image motion,” in Computer Vision, 1995. Proceedings. Fifth International Conference on. IEEE, 1995, pp. 374–381.

  • D. Ghimire, J. Lee, Z. N. Li, S. Jeong, S. H. Park, and H. S. Choi, “Recognition of facial expressions based on tracking and selection of discriminative geometric features,” Int. J. Multimed. Ubiquitous Eng., vol. 10, no. 3, pp. 35–44, 2015


Abstract Views: 463

PDF Views: 3




  • A Development of Emotion Recognition System

Abstract Views: 463  |  PDF Views: 3

Authors

Neenu Sharma
Gyan Ganga Institute of Technology and Sciences, Jabalpur (M.P.), India
Preeti Rai
Gyan Ganga Institute of Technology and Sciences, Jabalpur (M.P.), India

Abstract


Human Computer Interaction (HCI) is one of most interesting topic in machine visualization and image processing fields. Emotion recognition plays an important role in security and interpersonal communication. Biometric system helps in identification, security and authentication using face image. Recognize emotion of a person from occluded face image is a challenging task in emotion recognition. Feature are calculated for face image using Principe Component Analysis (PCA) and Two-Directional Two Dimension Principal Component Analysis [(2D) 2PCA] along with discrete wavelet transform. K-Nearest Neighbor (K-NN) and multiclass support vector machine used for classification of different emotion. This paper shows the comparative study of feature extraction and classification method. This study is performed in three dataset. JAFFE, CMU and CK database is used for calculating the classification rate of emotion recognition system .Resulting successful classification rate for JAFFE database is 91.8919% for CMU dataset classification rate is 70.339 % and for CK database resulting classification rate is 75.3073% using Multi class support vector machine. Multiclass support vector machine gives better result as compare to K-nearest neighbor.


Keywords


(2D) 2PCA (Two-Directional Two Dimension Principal Component Analysis), Principe Component Analysis (PCA), Multi Class Support Vector Machine (MSVM), K-Nearest Neighbor, JAFFE, CMU, CK Database.

References