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2019 | 121 | 83-89
Article title

A Comparative Study between CS-LBP/SVM and CS-LBP/PCA in Facial Expression Recognition

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EN
Abstracts
EN
Face plays significant role in social communication. This is a 'window' to human personality, emotions and thoughts. Due to this, face is a subject of study in many areas of science such as psychology, behavioral science, medicine and computer science etc. In this paper, a comparative study is suggested between CS-LBP/SVM and CS-LBP/PCA. These algorithms are used in emotive facial expression recognition. Finally, a comparison is shown between PCA & SVM in terms of Dimension Reduction. The proposed system uses grayscale frontal face images of a Japanese female to classify six basic emotions namely happiness, sadness, disgust, fear, surprise and anger.
Year
Volume
121
Pages
83-89
Physical description
Contributors
author
  • Mewar University, Chittorgarh, Rajasthan, India
References
  • [1] M. Hassaballah and Saleh Aly, Face Recognition: Challenges, Achievements and Future Directions. IET Journals of Computer Vision, Vol. 9, No. 4, pp. 614-626, 2015.
  • [2] S. Tolba, A. H. El-Baz, and A. A. El-Harby, Face Recognition: A Literature Review. International Journal of Computer, Electrical, Automation, Control and Information Engineering, Vol. 2, No. 7, pp. 2556-2571, 2008.
  • [3] T. Ojala, M. Pietikäinen and T. Mäenpää, “Multiresolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 24, No. 7, pp. 971–987, 2002.
  • [4] K. Meena, A. Suruliandi and R. Reena Rose. Face Recognition Based on Local Derivative Ternary Pattern. IETE Journal of Research, Vol. 60, No. 1, pp. 20-32, Feb. 2014.
  • [5] Shashi Kant Sharma, Maitreyee Dutta and Kota Solomon Raju, Comparative Study of Efficient Face Recognition Methods: A Literature Survey, in Proceedings of International Conference on Communication, Computing and Networking (ICCCN), Vol. 2, pp. 554-559, 2017. ISBN: 978-8-193-38970-6
  • [6] Shashi Kant Sharma, Maitreyee Dutta and Kota Solomon Raju, Comparative Study of Efficient Face Recognition Methods: A Literature Survey, in Proceedings of International Conference on Communication, Computing and Networking (ICCCN), Vol. 2, pp. 554-559, 201. ISBN: 978-8-193-38970-6
  • [7] Solomon Raju Kota, J.L.Raheja, Archana Rathi, Shashi Kant Sharma, Principal Component Analysis for Gesture Recognition using SystemC. International Conference on Advances in Recent Technologies in Communication and Computing 2009 IEEE pp.732-737. DOI: 10.1109/ARTCom.2009.177
  • [8] Tofighi, N. Khairdoost, S. A. Monadjemi and K. Jamshidi, A Robust Face Recognition System in Image and Video. I. J. Image, Graphics and Signal Processing, Vol. 8, pp. 1-11, 2014.
  • [9] Mahesh Kumar Sharma, Shashikant Sharma, NopbhornLeeprechanon, and AashishRanjan. Wavelet decomposition based principal component analysis for face recognition using MATLAB. Advancement in Science and Technology AIP Conf. Proc. 1715, 020055-1–020055-10; pp. 1-10. doi:10.1063/1.4942737
  • [10] Shashi Kant Sharma, Kota Solomon Raju, Application of Gaussian Filter with Principal Component Analysis Algorithm for the Efficient Face Recognition. International Journal of Electronics and Communication Engineering & Technology Volume 4, Issue 7 (2013) pp. 244-251.
  • [11] Wiskott, L., Fellous, J. M., Kuiger, N. and Von Der Malsburg, C. Face recognition by Elastic Bunch Graph Matching. Pattern Analysis and Machine Intelligence. IEEE Transaction on Pattern Analysis and Machine Intelligence, 19(7), pp. 775-779, 1997.
  • [12] Manish Shankar Kaushik and Aditya Bihar Kandali, Recognition of Facial Expression extracting Salient Features using Local Binary Pattens and Histogram of Oriented Gradients, in IEEE International conference on Energy, Communication, Data Analytic and Soft Computing (ICECDS), pp. 1201-1205, 2017.
Document Type
article
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YADDA identifier
bwmeta1.element.psjd-c0ca480c-96eb-4247-bb0c-ee137fab9f78
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