Abstract
This research study performed on visible images, thermal images and fused images for facial expression recognition. Linear Discriminant Analysis has implemented for feature extraction technique and support vector machine to calculate the result. This work is implemented on a newly designed database of 20 peoples’ facial expression which includes visible images, thermal images, and fused images. The extracted features of visible, thermal and fused images are utilized for classification using support vector machine. This study focuses on 5 types of facial expression. Better results are achieved on smile and anger expression. The comparative analysis of this study is done on visible, thermal and fused facial expression images. The experimental result analysis shows that fused images give better results as compared to visible images. The accuracy of smile expression is better than anger and disgust facial expression. The implementation is carried out on dataset designed in indoor and outdoor environmental setup.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhang, Z.: Feature-based facial expression recognition: sensitivity analysis and experiments with a multi-layer perceptron. Int. J. Pattern Recognit. Artif. Intell. 13(6), 893–911 (1999)
Chibelushi, C.C., Bourel, F.: Facial expression recognition: a brief tutorial overview (2002)
Wang, S. (IEEE Member), Liu, Z., Lv, S., Lv, Y., Wu, G., Peng, P., Chen, F., Wang, X.: A natural visible and infrared facial expression database for expression recognition and emotion inference. https://doi.org/10.1109/TMM.2010.2060716
Nguyen, H., Chen, F., Kotani, K.: Fusion of visible images and thermal image sequences for automated facial emotion estimation. J. Mob. Multimed. 10(3&4), 294–308 (2014). Rinton Press
Bebis, G., Gyaourova, A., Singh, S., Pavlidis, I.: Face recognition by fusing thermal infrared and visible imagery. Image Vis. Comput. 24, 727–742 (2006). Elsevier
Shen, P., Wang, S., et al.: Facial expression recognition from thermal video. In: Intelligent Autonomous System 12. AISC, vol. 194, pp. 323–333. Springer, Heidelberg (2013)
Wang, S., He, S.: Spontaneous facial expression recognition by fusing thermal infrared and visible images. In: Intelligent Autonomous System 12. AISC, vol. 194, pp. 323–333. Springer, Berlin (2013)
Ross, A., Jain, A.: Information fusion in biometrics. Pattern Recognit. Lett. 24, 2115–2125 (2003)
Monwar, Md.M., Gavrilova, M.L.: Multimodal biometric system using rank-level fusion approach. IEEE Trans. Syst. Man Cybern.—Part B: Cybernet. 39(4), 867–878 (2009)
Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Technol. 14, 420 (2004)
Jain, A.K., Ross, A.: Multibiometric systems. IEEE Trans. 47, 34–40 (2004)
Sheena, S., Mathu, S.: A study of multimodal biometric system. IJRET 3, 93–97 (2014)
Deriche, M.: Trends and challenges in mono and multi biometrics. In: IEEE Image Processing Theory, Tools and Applications, pp. 1–9 (2008)
Ives, R.W.: A multidisciplinary approach to biometrics. IEEE Trans. Educ. 48, 462–472 (2005)
Prabhakar, S., Kittler, J., Maltoni, D., OGorman, L., Tan, T.: Introduction to the special issue on biometrics: progress and directions. IEEE Trans. Pattern Anal. Mach. Intell. 29, 513–516 (2007)
Karande, K.J., Talbar, S.N.: Simplified and modified approach for face recognition using PCA. In: IET-UK ICTES 2007, Dr. M.G.R. University, Chennai, Tamil Nadu, India, pp. 523–526 (2007)
Zhao, H., Yuen, P.C., Member, I.E.E.E., Kwok, J.T.: A novel incremental principal component analysis and its application for face recognition. IEEE Trans. Syst. Man Cybernet. Part B: Cybernet. 36(4), 873–887 (2006)
Hermosilla, G., Ruiz-del-Solar, J., Verschae, R., Correa, M.: A comparative study of thermal face recognition methods in unconstrained environments in Elsevier. Pattern Recognit. 45, 452–459 (2012)
Kumar, M.G.S., Saravanan, D.: A novel approach to face recognition based on thermal imaging. IJRET 03(03), 141–145 (2014)
Bhowmik, M.K., Saha, K., Majumder, S., Majumder, G., Saha, A., Sarma, A.N.: Thermal infrared face recognition a biometric identification technique for robust security system. In: Intech, pp. 113–139
https://en.wikipedia.org/wiki/File:AirportThermographicCamera.jpg
https://www.flir.com/globalassets/imported-assets/document/flir-ets320-usermanual.pdf, pp. 69–70
Ruggieri, S.: Efficient C4.5. IEEE Trans. Knowl. Data Eng. 14(2), 438–444 (2002)
Kwon, O., Sim, J.M.: Effects of data set features on the performances of classification algorithms. Expert Syst. Appl. 40, 1847–1857 (2013)
Socolinsky, D., Selinger, A., Neuheisel, J.: Face recognition with visible and thermal infrared imagery. Comput. Vis. Image Underst. 91, 72–114 (2003)
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–96 (1991)
Kakarwal, S.N.: Development of feature extraction techniques for face recognition. Ph.D. thesis, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India (2012)
Uddin, Md.Z. (IEEE Senior Member), Khaksar, W., Torresen, J. (IEEE Senior Member): Facial expression recognition using salient features and convolutional neural networking. In: IEEE (2017). https://doi.org/10.1109/access.2017.2777003
Ghimire, D., Lee, J., Li, Z.-N., Jeong, S.: Recognition of facial expressions based on salient geometric features and support vector machines. Multimed. Tools Appl. 2017(76), 7921–7946 (2017)
Lajevardi, S.M., Hussain, Z.M.: Automatic facial expression recognition: feature extraction and selection. SIViP 6, 159–169 (2012). Springer
Samad, R., Sawada, H.: Extraction of the minimum number of Gabor wavelet parameters for the recognition of natural facial expressions. Artif. Life Robot. 16, 21–31 (2011). https://doi.org/10.1007/s10015-011-0871-6
Tsai, H.-H., Chang, Y.-C.: Facial expression recognition using a combination of multiple facial features and support vector machine. Soft Comput. (2017). https://doi.org/10.1007/s00500-017-2634-3. Springer, Berlin
Patil, R.A., Sahula, V., Mandal, A.S.: Features classification using geometrical deformation feature vector of support vector machine and active appearance algorithm for automatic facial expression recognition. Mach. Vis. Appl. 25, 747–761 (2014)
Belarbi, M.A., Mahmoudi, S., Belalem, G.: PCA as dimensionality reduction for large-scale image retrieval systems. Int. J. Ambient. Comput. Intell. (IJACI) 8(4), 45–58 (2017)
Dey, N., Wagh, S., Mahalle, P., Pathan, M. (ed.): Applied Machine Learning for Smart Data Analysis. CRC Press, Boca Raton. https://doi.org/10.1201/9780429440953
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Patil, R., Chaudhari, K., Kakarwal, S.N., Deshmukh, R.R., Kurmude, D.V. (2020). Analysis of Facial Expression Recognition of Visible, Thermal and Fused Imaginary in Indoor and Outdoor Environment. In: Dey, N., Mahalle, P., Shafi, P., Kimabahune, V., Hassanien, A. (eds) Internet of Things, Smart Computing and Technology: A Roadmap Ahead. Studies in Systems, Decision and Control, vol 266. Springer, Cham. https://doi.org/10.1007/978-3-030-39047-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-39047-1_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-39046-4
Online ISBN: 978-3-030-39047-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)