Document Type : Research Paper

Authors

Department of MCA, School of Computer Science and IT, Jain (deemed-to-be) University, Bangalore, Karnataka, India.

Abstract

This paper consists of analysis of an algorithm dealing with facial expressions recognition. The algorithm has three major steps, initially image is processed, then the facial features are extracted and finally facial expression is recognized. In the initial processing stage the facial region is identified using a Haar cascade classifier. This facial region is passed on to the model trained by a CNN where facial features are matched with the features specified in the model. In the final step on the basis of comparison in the previous step the image is labelled and results are displayed. By the experiment results it is clear that the method specified in the paper can detect facial expressions very well.

Keywords

Main Subjects

[1]   Broumi, S., Dey, A., Talea, M., Bakali, A., Smarandache, F., Nagarajan, D., ... & Kumar, R. (2019). Shortest path problem using Bellman algorithm under neutrosophic environment. Complex & intelligent systems5(4), 409-416.

[2]   Kumar, R., Dey, A., Broumi, S., & Smarandache, F. (2020). A study of neutrosophic shortest path problem. In Neutrosophic graph theory and algorithms (pp. 148-179). IGI Global.

[3]   Kumar, R., Edalatpanah, S. A., Jha, S., Broumi, S., Singh, R., & Dey, A. (2019). A multi objective programming approach to solve integer valued neutrosophic shortest path problems. Neutrosophic sets and systems, 24 (pp. 139-151). University of New Mexico. DOI: 10.5281/zenodo.2595968

[4]   Kumar, R., Edalatpanah, S. A., Jha, S., & Singh, R. (2019). A novel approach to solve gaussian valued neutrosophic shortest path problems. International journal of engineering and advanced technology, 8(3), 347-353. file:///C:/Users/jpour/Downloads/2019-ANovelApproachtoSolveGaussianValuedNeutrosophicShortestPathProblems.pdf

[5]   Kumar, R., Edaltpanah, S. A., Jha, S., Broumi, S., & Dey, A. (2018). Neutrosophic shortest path problem. Neutrosophic sets and systems, 23 (pp. 5-15). University of New Mexico.

[6]   Pratihar, J., Kumar, R., Dey, A., & Broumi, S. (2020). Transportation problem in neutrosophic environment. In Neutrosophic graph theory and algorithms (pp. 180-212). IGI Global.

[7]   Kumar, R., Edalatpanah, S. A., Jha, S., & Singh, R. (2019). A Pythagorean fuzzy approach to the transportation problem. Complex & intelligent systems5(2), 255-263.

[8]   Pratihar, J., Kumar, R., Edalatpanah, S. A., & Dey, A. (2020). Modified Vogel’s approximation method for transportation problem under uncertain environment. Complex & intelligent systems, 1-12. https:doi.org10.1007s40747-020-00153-4

[9]   Gayen, S., Jha, S., Singh, M., & Kumar, R. (2019). On a generalized notion of anti-fuzzy subgroup and some characterizations. International journal of engineering and advanced technology8, 385-390.

[10]    Gayen, S., Smarandache, F., Jha, S., & Kumar, R. (2020). Interval-valued neutrosophic subgroup based on interval-valued triple t-norm. In Neutrosophic sets in decision analysis and operations research (pp. 215-243). IGI Global.

[11]    Gayen, S., Smarandache, F., Jha, S., Singh, M. K., Broumi, S., & Kumar, R. (2020). Introduction to plithogenic subgroup. In Neutrosophic graph theory and algorithms (pp. 213-259). IGI Global.

[12]    Gayen, S., Smarandache, F., Jha, S., Singh, M. K., Broumi, S., & Kumar, R. (2020). Soft subring theory under interval-valued neutrosophic environment. Neutrosophic Sets and Systems, 36 (pp. 193-219). University of New Mexico.

[13]    Gayen, S., Smarandache, F., Jha, S., & Kumar, R. (2020). Introduction to interval-valued neutrosophic subring. Neutrosophic sets and systems, 36 (pp. 220-245). University of New Mexico.

[14]    Gayen, S., Smarandache, F., Jha, S., Singh, M. K., Broumi, S., Kumar, R. (2020). Introduction to plithogenic hypersoft subgroup. Neutrosophic sets and systems, 33 (pp. 208-233). University of New Mexico.

[15]    Yang, Y., Yan, D., & Zhao, J. (2017). Optimal path selection approach for fuzzy reliable shortest path problem. Journal of intelligent & fuzzy systems32(1), 197-205.

[16]    Kumar, R., Jha, S., & Singh, R. (2020). A different approach for solving the shortest path problem under mixed fuzzy environment. International journal of fuzzy system applications (IJFSA)9(2), 132-161.

[17]    Kumar, R., Jha, S., & Singh, R. (2017). Shortest path problem in network with type-2 triangular fuzzy arc length. Journal of applied research on industrial engineering4(1), 1-7.

[18]    Kumar, R., Edalatpanah, S. A., Jha, S., Gayen, S., & Singh, R. (2019). Shortest path problems using fuzzy weighted arc length. International journal of innovative technology and exploring engineering8(6), 724-731.

[19]    Singh, A., Kumar, A., & Appadoo, S. S. (2019). A novel method for solving the fully neutrosophic linear programming problems: Suggested modifications. Journal of intelligent & fuzzy systems37(1), 885-895.

[20]    Mohapatra, H., Panda, S., Rath, A., Edalatpanah, S., & Kumar, R. (2020). A tutorial on powershell pipeline and its loopholes. International journal of emerging trends in engineering research8(4), 975-982.

[21]    Mohapatra, H., Rath, S., Panda, S., & Kumar, R. (2020). Handling of man-in-the-middle attack in wsn through intrusion detection system. International journal8(5), 1503-1510.

[22]    Mohapatra, H., Debnath, S., & Rath, A. K. (2019). Energy management in wireless sensor network through EB-LEACH. International journal of research and analytical reviews (IJRAR), 56-61. DOI: 10.1729/Journal.21701

[23]    Mohapatra, H., Rath, A. K., Landge, P. B., & Bhise, D. A.  (2020). Comparative Analysis of Clustering Protocols of Wireless Sensor Network. International journal of mechanical and production engineering research and development (IJMPERD) ISSN (P), 10(3), 2249-6890.

[24]    Mohapatra, H., & Rath, A. K. (2020). Survey on fault tolerance-based clustering evolution in WSN. IET networks, 9(4), 145-155.

[25]    Mohapatra, H., Debnath, S., Rath, A. K., Landge, P. B., Gayen, S., & Kumar, R. (2020). An efficient energy saving scheme through sorting technique for wireless sensor network. International journal8(8), 4278-4286.

[26]    Mohapatra, H., & Rath, A. K. (2020). Fault tolerance in wsn through uniform load distribution function. International journal of sensors, wireless communications and control10(1), 1-10. https://doi.org/10.2174/2210327910999200525164954

[27]    Mohapatra, H., & Rath, A. K. (2019). Fault tolerance through energy balanced cluster formation (EBCF) in WSN. In Smart innovations in communication and computational sciences (pp. 313-321). Springer, Singapore.

[28]    Mohapatra, H., & Rath, A. K. (2019). Fault tolerance in WSN through PE-LEACH protocol. IET wireless sensor systems9(6), 358-365.

[29]    Mohapatra, H. (2018). C Programming: practice. Amazon.

[30]    Mohapatra, H., & Rath, A. K. (2020). Fundamentals of software engineering. BPB.

[31]    Mohapatra, H. (2009). HCR by using neural network (Master's thesis, M.Tech_s Desertion, Govt. College of Engineering and Technology, Bhubaneswar).

[32]    Panda, M., Pradhan, P., Mohapatra, H., & Barpanda, N. K. (2019). Fault tolerant routing in heterogeneous environment. International journal of scientific & technology research8(8), 1009-1013.

[33]    Nirgude, V. N., Nirgude, V. N., Mahapatra, H., Shivarkar, S. A. (2017). Face recognition system using principal component analysis & linear discriminant analysis method simultaneously with 3d morphable model and neural network BPNN method. Global journal of advanced engineering technologies and sciences, 4(1), 1-6.

[34]    Mohapatra, H., & Rath, A. K. (2020, October). Nub less sensor based smart water tap for preventing water loss at public stand posts. 2020 IEEE microwave theory and techniques in wireless communications (MTTW) (Vol. 1, pp. 145-150). IEEE. DOI: 10.1109/MTTW51045.2020.9244926

[35]    Mohapatra, H., Rath, A. K. (2020). IoT-based smart water. In IOT technologies in smart-cities: from sensors to big data, security and trust. DOI: 10.1049/PBCE128E

[36]    Mohapatra, H. (2020). Offline drone instrumentalized ambulance for emergency situations. International journal of robotics and automation (IJRA)9(4), 251-255.

[37]    Mohapatra, H., & Rath, A. K. (2019). Detection and avoidance of water loss through municipality taps in India by using smart taps and ICT. IET wireless sensor systems9(6), 447-457.

[38]    Panda, H., Mohapatra, H., & Rath, A. K. (2020). WSN-Based Water Channelization: An Approach of Smart Water. In Smart cities—opportunities and challenges (pp. 157-166). Springer, Singapore.

[39]    Li, S., & Deng, W. (2020). Deep facial expression recognition: A survey. IEEE transactions on affective computing, 3045, 1–20. DOI: 10.1109/TAFFC.2020.2981446

[40]    Gogić, I., Manhart, M., Pandžić, I. S., & Ahlberg, J. (2020). Fast facial expression recognition using local binary features and shallow neural networks. The visual computer36(1), 97-112.

[41]    Carranza, K. A. L. R., Manalili, J., Bugtai, N. T., & Baldovino, R. G. (2019, November). Expression tracking with OpenCV deep learning for a development of emotionally aware Chatbots. 2019 7th international conference on robot intelligence technology and applications (RiTA) (pp. 160-163). IEEE.

[42]    Li, Z., Han, S., Khan, A. S., Cai, J., Meng, Z., O'Reilly, J., & Tong, Y. (2019, July). Pooling map adaptation in convolutional neural network for facial expression recognition. 2019 IEEE international conference on multimedia and expo (ICME) (pp. 1108-1113). IEEE.

[43]    Krithika, L. B., & Priya, G. L. (2020). Graph based feature extraction and hybrid classification approach for facial expression recognition. Journal of ambient intelligence and humanized computing, 1-17. https://doi.org/10.1007/s12652-020-02311-5

[44]    Majumder, A., Behera, L., Member, S., Subramanian, V. K. (2016). Automatic facial expression recognition system using deep network-based data fusion, 48(1), 103–114.

[45]    Tong, Y., Liao, W., & Ji, Q. (2007). Facial action unit recognition by exploiting their dynamic and semantic relationships. IEEE transactions on pattern analysis and machine intelligence29(10), 1683-1699.

[46]    Mollahosseini, A., Chan, D., & Mahoor, M. H. (2016, March). Going deeper in facial expression recognition using deep neural networks. 2016 IEEE winter conference on applications of computer vision (WACV) (pp. 1-10). IEEE.

[47]    Liu, K., Zhang, M., & Pan, Z. (2016, September). Facial expression recognition with CNN ensemble. 2016 international conference on cyberworlds (CW) (pp. 163-166). IEEE.

[48]    Xie, S., Hu, H., & Wu, Y. (2019). Deep multi-path convolutional neural network joint with salient region attention for facial expression recognition. Pattern recognition92, 177-191.

[49]    An, F., & Liu, Z. (2020). Facial expression recognition algorithm based on parameter adaptive initialization of CNN and LSTM. The visual computer36(3), 483-498. https://doi.org/10.1007/s00371-019-01635-4

[50]    Kulkarni, K. R., & Bagal, S. B. (2016). Facial expression recognition. 12th IEEE international conference electronics, energy, environment, communication, computer, control: (E3-C3), INDICON 2015 (pp. 1-5). IEEE. DOI: 10.1109/INDICON.2015.7443572

[51]    Yu, Z., & Zhang, C. (2015, November). Image based static facial expression recognition with multiple deep network learning. Proceedings of the 2015 ACM on international conference on multimodal interaction (pp. 435-442).

[52]    Happy, S. L., & Routray, A. (2014). Automatic facial expression recognition using features of salient facial patches. IEEE transactions on affective computing6(1), 1-12.

[53]    Shin, M., Kim, M., & Kwon, D. S. (2016, August). Baseline CNN structure analysis for facial expression recognition. 2016 25th IEEE international symposium on robot and human interactive communication (RO-MAN) (pp. 724-729). IEEE.

[54]    Lopes, A. T., de Aguiar, E., De Souza, A. F., & Oliveira-Santos, T. (2017). Facial expression recognition with convolutional neural networks: coping with few data and the training sample order. Pattern recognition61, 610-628.

[55]    Li, K., Jin, Y., Akram, M. W., Han, R., & Chen, J. (2020). Facial expression recognition with convolutional neural networks via a new face cropping and rotation strategy. The visual computer36(2), 391-404.

[56]    Shao, J., & Qian, Y. (2019). Three convolutional neural network models for facial expression recognition in the wild. Neurocomputing355, 82-92. https://doi.org/10.1016/j.neucom.2019.05.005

[57]    Kim, J. H., Kim, B. G., Roy, P. P., & Jeong, D. M. (2019). Efficient facial expression recognition algorithm based on hierarchical deep neural network structure. IEEE access7, 41273-41285.DOI: 10.1109/ACCESS.2019.2907327

[58]    Yang, S., Luo, P., Loy, C. C., & Tang, X. (2016). Wider face: A face detection benchmark. Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), (pp. 5525-5533).

[59]    Wu, C., Chai, L., Yang, J., & Sheng, Y. (2019, July). Facial expression recognition using convolutional neural network on graphs. 2019 Chinese control conference (CCC) (pp. 7572-7576). IEEE.