Abstract
Facial gesture recognition is an application based on pattern recognition which has applications in expression identification in criminal cases, investigation, and security purpose, human–machine interaction but still accuracy, illumination and occlusion are the research issues which have to improve. Key research issue of facial gesture identification is improving the accuracy of system which is measured in term of recognition rate and accuracy of system mostly depends on optimization of feature extraction. In the facial gesture, edge pattern (shape) and texture are unique pattern which have to extract from facial image. In this paper, Gabor filter is used to extract edge pattern from face but Gabor produces high-dimensional matrix with redundant edge information which is reduced optimally by proposed maximum discrete cosine transformation in order to improve accuracy of system.
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References
Zheng Zhang “Wavelet Decomposition and Adaboost Feature Weighting For Facial Expression Recognition.” International Conference on Control, Automation and Systems Engineering (CASE), pp. 1–4, 30–31, 2011.
Valstar, Michel. “Automatic facial expression analysis.” In Understanding Facial Expressions in Communication, pp. 143–172. Springer India, 2015.
Shilpa Choudhary, Kamlesh Lakhwani, and Shubhlakshmi Agrwal. “An efficient hybrid technique of feature extraction for facial expression recognition using AdaBoost Classifier.” International Journal of Engineering Research & Technology, issue: 1, vol no. 8, 2012.
Boles, Wageeh W., and Boualem Boashash. “A human identification technique using images of the iris and wavelet transform.” IEEE transactions on signal processing 46, no. 4 (1998): 1185–1188.
Shubh Lakshmi Agrwal, Meeta Sharmsa, Deeksha Kumari, and Sandeep Kumar Gupta. “Improved image compression technique using IWT-DCT transformation.” In Next Generation Computing Technologies (NGCT), 2016 2nd International Conference on, pp. 683–686. IEEE, 2016.
Soni, Karuna, Sandeep K. Gupta, Umesh Kumar, and Shubh L. Agrwal. “A new Gabor wavelet transform feature extraction technique for ear biometric recognition.” In Power India International Conference (PIICON), 2014 6th IEEE, pp. 1–3. IEEE, 2014.
Blessy Chacko, Shubh L. Agrwal, Sandeep K. Gupta, Saurabh R. Srivastava and Neha Srivastav, “Performance of Image Fusion Technique Using 4x4 Block Wavelet Cosine Transformation”, In Clouds Computing, Data Science & Engineering (CONFLENCE), 7th International Conference on, IEEE, 2017.
Dosodia, Priya, Amarjeet Poonia, Sandeep K. Gupta, and Shubh Lakshmi Agrwal. “New Gabor-DCT feature extraction technique for facial expression recognition.” In Communication Systems and Network Technologies (CSNT), 2015 Fifth International Conference on, pp. 546–549. IEEE, 2015.
Kulkani, Sameer S., John Moriarty, and Chih-Cheng Hung. “The impact of Image block size on face feature extraction using discrete cosine transform.” In IEEE Proceedings of the SoutheastCon 2010 (SoutheastCon), IEEE, 2010.
Ramasubramanian, D., and Y. V. Venkatesh. “Encoding and recognition of faces based on the human visual model and DCT.” Pattern recognition 34, no. 12 (2001): 2447–2458.
Shubh Lakshmi Agrwal, Anita Yadav, Umesh Kumar, and Sandeep Kumar Gupta. “Improved invisible watermarking technique using IWT-DCT.” In Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2016 5th International Conference on, pp. 283–285. IEEE, 2016.
Gupta, Sandeep K., ShubhLakshmi Agrwal, Yogesh K. Meena, and Neeta Nain. “A hybrid method of feature extraction for facial expression recognition.” In Signal-Image Technology and Internet-Based Systems (SITIS), 2011 Seventh International Conference on, pp. 422–425. IEEE, 2011.
Verma, Deepak, Vijaypal Dhaka, and Shubhlakshmi Agrwal. “An Improved Average Gabor Wavelet Filter Feature Extraction Technique for Facial Expression Recognition.” International Journal on Innovations in Engineering and Technology 2: 2319–1058.
Tariq, Usman, and Thomas S. Huang. “Features and fusion for expression recognition—A comparative analysis.” In 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 146–152. IEEE, 2012.
Burrus, C. Sidney, Ramesh A. Gopinath, and Haitao Guo. “Introduction to wavelets and wavelet transforms: a primer.” (1997).
Shan, Caifeng, Shaogang Gong, and Peter W. McOwan. “Facial expression recognition based on local binary patterns: A comprehensive study.” Image and Vision Computing 27, no. 6 (2009): 803–816.
Jun Ou, Xiao-Bo Bai*, Yun Pei, Liang Ma, Wei Liu “Automatic Facial Expression Recognition Using Gabor Filter And Expression Analysis”, in Second International Conference on Computer Modeling and Simulation, pp. 216–218, IEEE, 2010.
Lajevardi, Seyed Mehdi, and Margaret Lech. “Facial expression recognition using neural networks and log-Gabor filters.” In Digital Image Computing: Techniques and Applications (DICTA), 2008, pp. 77–83. IEEE, 2008.
Binjiang, Guo-Sheng Yang, Huan-Long Zhang. “Comparative Study Of Dimension Reduction And Recognition Algorithms Of Dct And 2dpca”, Proceedings Of The Seventh International Conference On Machine Learning And Cybernetics, Kunming, pp. 401–411, 2008.
Shermina. J., “Illumination Invariant Face Recognition Using Discrete Cosine Transform and Principal Component Analysis”, International Conference on Emerging Trends in Electrical and Computer Technology (ICETECT), pp. 826–830, 23–24 March 2011.
Rabab M. Ramadan and Rehab F. Abdel – Kader “Face Recognition Using Particle Swarm Optimization-Based Selected Features”, International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 2, no. 2, pp 51–67, June 2009.
Shuai-Shi Liu, Yan-Tao Tian, Dong Li, “New Research Advances Of Facial Expression Recognition”, Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, Baoding, 12–15 July 2009.
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Gupta, S.K., Sharma, A., Prajapati, A., Agrwal, S.L., Garg, N. (2018). Gabor-Max-DCT Feature Extraction Techniques for Facial Gesture Recognition. In: Perez, G., Tiwari, S., Trivedi, M., Mishra, K. (eds) Ambient Communications and Computer Systems. Advances in Intelligent Systems and Computing, vol 696. Springer, Singapore. https://doi.org/10.1007/978-981-10-7386-1_64
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