Emotion Recognition for Challenged People Facial Appearance in Social using Neural Network

Emotion Recognition for Challenged People Facial Appearance in Social using Neural Network
  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2022 by IJETT Journal
Volume-70 Issue-6
Year of Publication : 2022
Authors : P. Deivendran, P. Suresh Babu, G. Malathi, K. Anbazhagan, R. Senthil Kumar
DOI : 10.14445/22315381/IJETT-V70I6P228

How to Cite?

P. Deivendran, P. Suresh Babu, G. Malathi, K. Anbazhagan, R. Senthil Kumar, "Emotion Recognition for Challenged People Facial Appearance in Social using Neural Network," International Journal of Engineering Trends and Technology, vol. 70, no. 6, pp. 272-278, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I6P228

Abstract
Human communication is the vocal and non-verbal signal to communicate with others. Human expression is a significant biometric object in picture and record databases of surveillance systems. Face appreciation has a serious role in biometric methods and is good-looking for plentiful applications, including visual scrutiny and security. Facial expressions are a form of nonverbal communication; recognizing them helps improve the human-machine interaction. This paper proposes an idea for face and enlightenment invariant credit of facial expressions by the images. In order on, the person's face can be computed. Face expression is used in CNN (Convolutional Neural Network) classifier to categorize the acquired picture into different emotion categories. It’s a deep, feed-forward artificial neural network. Outcome surpasses human presentation and shows poses alternate performance. Varying lighting conditions can influence the fitting process and reduce recognition precision. Results illustrate that dependable facial appearance credited with changing lighting conditions for separating reasonable facial terminology display emotions is an efficient representation of clean and assorted moving expressions. This process can also manage the proportions of dissimilar basic affecting expressions of those mixed jointly to produce sensible emotional facial expressions. Our system contains a pre-defined data set, which was residential by a statistics scientist and includes all pure and varied expressions. On average, a data set has achieved 92.4% exact validation of the expressions synthesized by our technique. These facial expressions are compared through the pre-defined data-position inside our system. If it recognizes the person in an abnormal condition, an alert will be passed to the nearby hospital/doctor seeing that a message

Keywords
Facial expression mapping, Image recognition, Convolutional Neural Network. Image, classification, emotion, data set.

Reference
[1] M. Song Et Al., A Generic Framework for Efficient 2-D and 3-D Facial Expression Analogy, Ieee Trans. Multimedia, 9(7) (2017) 1384–1395.
[2] A. Asthana, M. De La Hunty, A. Dhall, and R. Goecke, Facial Performance Transfer Via Deformable Models and Parametric Correspondence,Ieee Trans. Vis. Comput. Graph., 18(9) (2020) 1511–1519.
[3] K. Li, F. Xu, J. Wang, Q. Dai, and Y. Liu, A Data-Driven Approach for Facial Expression Synthesis in Video, in Proc. Ieee Conf. Comput. Vis. Pattern Recognit., 11(3) (2019)57–64.
[4] K. Li Et Al., A Data-Driven Approach for Facial Expression Retargeting in Video,Ieee Trans. Multimedia, 16(2) (2017) 299–310.
[5] Deivendran, P,Naganathan, E.R, Scalability Assurance Process in Replication and Migration Using Cloud Simulator, International Journal of Networking and Virtual Organisations, 21(1) (2019) 112-126.
[6] L. Xiong, N. Zheng, Q. You, and J. Liu, Facial Expression Sequence Synthesis Based on Shape and Texture Fusion Model, in Proc. Ieee Int. Conf. Image Process, 4(3) (2019) 473-476.
[7] D. Huang and F. De La Torre, Bilinear Kernel Reduced Rank Regression for Facial Expression Synthesis, in Proc. Eur. Conf. Comput. 6(2) (2018) 364– 377.
[8] S. Agarwal and D. P. Mukherjee, Decoding Mixed Emotions From Expression Map of Face Images, in Proc. Int. Conf. Workshops Autom. Face Gesture Recognit., 1(6) 2018. Doi. 10.1109/Fg.2013.6553731.
[9] S. Agarwal, M. Chatterjee, and D. P. Mukherjee, Synthesis of Emotional Expressions Specific to Facial Structure, in Proc. 8th Indian Conf. Vis., Graph. Image Process., 9(2) (2019) 28-36. Doi. 10.1145/2425333.2425361.
[10] S. Haykin, Neural Networks A Comprehensive Foundation. New Delhi, India: Dorling Kindersley (India) Pvt. Ltd., 11(4) (2021) 162-168.
[11] Deivendran, P, Naganathan, E.R, ,Enterprise architecture Frameworks and Services for Cloud Computing, International Journal of Cloud Computing and Services Science, 4(2) (2018) 176-181.
[12] Issam Dagher,‖Incremental Pca-Lda Algorithm‖, International Journal of Biometrics and Bioinformatics (Ijbb), 4(2) (2021) 210-215.
[13] J. Shermina,V. Vasudevan,‖An Efficient Face Recognition System Based on Fusion of Mpca and Lpp‖, American Journal of Scientific Research 11(4) (2018) 6-19.
[14] Ishwar S. Jadhav, V. T. Gaikwad, Gajanan U. Patil,‖Human Identification Using Face and Voice Recognition‖, International Journal of Computer Science and Information Technologies, 2 (3) (2019) 65-69
[15] Yun-Hee Han,Keun-Chang Kwak,‖ Face Recognition and Representation By Tensor-Based Mpca Approach‖, the 3rd International Conference on Machine Vision , (2020) 84-88.
[16] Victor M. Alvarez, Ramiro Velázquez, Sebastián Gutierrez, Josué Enriquez-Zarate ,A Method for Facial Emotion Recognition Based on Interest Points,in International Conference on Research in Intelligent and Computing in Engineering (Rice), 9(11) ( 2018) 54-60.
[17] Dumas, Melanie, Emotional Expression Recognition Using Support Vector Machines,International Journal of Computer Science, 7(2) ( 2021) 32-36.
[18] Muzammil, Abdulrahman, Facial Expression Recognition Using Support Vector Machines, in 23nd Signal Processing and Communications Applications Conference (Siu) Ieee, 8(2) (2019).
[19] Turabzadeh, Saeed & Meng, Hongying & Swash, M. & Pleva, Matus & Juhár, Jozef, Facial Expression Emotion Detection for Real-Time Embedded Systems, 5(12) ( 2018) 130-135.
[20] Byoung Chul Ko, A Brief Review of Facial Emotion Recognition Based on Visual Information, (2020).
[21] Agrawal Et N. Mittal, Using Cnn for Facial Expression Recognition: A Study of the Effects of Kernel Size and Number of Filters on Accuracy, Vis. Comput., 7(10) ( 2019). Doi: 10.1007/S00371-019-01630-9.
[22] D. K. Jain, P. Shamsolmoali, Et P. Sehdev,Extended Deep Neural Network for Facial Emotion Recognition, Pattern Recognit. Lett., 7(20) (2019) 69-74. Doi: 10.1016/J.Patrec.2019.01.008.
[23] D. H. Kim, W. J. Baddar, J. Jang, Et Y. M. Ro, Multi-Objective Based Spatio-Temporal Feature Representation Learning Robust to Expression Intensity Variations for Facial Expression Recognition , Ieee Trans. Affect. Comput., 10(2) (2019) 223‑236.
[24] Z. Yu, G. Liu, Q. Liu, Et J. Deng, Spatio-Temporal Convolutional Features with Nested Lstm for Facial Expression Recognition, Neurocomputing, 317(5) (2018) 50‑57.
[25] D. Dhinakaran, P.M. Joe Prathap, D. Selvaraj, D. Arul Kumar and B. Murugeshwari, Mining Privacy-Preserving Association Rules Based on Parallel Processing in Cloud Computing, International Journal of Engineering Trends and Technology, 70(3) (2022) 284-294. Https://Doi.Org/10.14445/22315381/Ijett-V70i3p232.
[26] D. Dhinakaran and P. M. Joe Prathap, Preserving Data Confidentiality in Association Rule Mining Using Data Share Allocator Algorithm, Intelligent Automation & Soft Computing, 33(3) (2022) 1877– 1892. Doi:10.32604/Iasc.2022.024509.
[27] D. Dhinakaran and P.M. Joe Prathap, Ensuring Privacy of Data and Mined Results of Data Possessor in Collaborative Arm, Pervasive Computing and Social Networking. Lecture Notes in Networks and Systems, Springer, Singapore, 317 (2022) 431 – 444. Doi: 10.1007/978-981-16-5640-8_34.
[28] D. Liang, H. Liang, Z. Yu, Et Y. Zhang, Deep Convolutional Bilstm Fusion Network for Facial Expression Recognition, Vis. Comput., 36(3) (2020) 499-508.
[29] Dr. Surendiran, R., Et Al. A Systematic Review Using Machine Learning Algorithms for Predicting Preterm Birth. International Journal of Engineering Trends and Technology, 70(5) (2022) 46-59. Crossref, Https://Doi.Org/10.14445/22315381/Ijett