Patch-SIFT: Enhanced feature descriptor to learn human facial emotions using an Ensemble approach

D nbsp Hema        nbsp   nbsp; nbsp S nbsp Kannan nbsp

doi:10.17485/IJST/v14i21.2261

Article

Patch-SIFT: Enhanced feature descriptor to learn human facial emotions using an Ensemble approach

VIEWS 2208
PDF 285

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v14i21.2261

Year: 2021, Volume: 14, Issue: 21, Pages: 1740-1747

Original Article

Patch-SIFT: Enhanced feature descriptor to learn human facial emotions using an Ensemble approach

D Hema ✉ ¹, S Kannan ²

¹Assistant Professor, Department of Computer Science, Lady Doak College, Madurai

²Professor, Department of Computer Applications, School of Information Technology, Kamaraj University, Madurai

Received Date:20 December 2020, Accepted Date:28 May 2021, Published Date:17 June 2021

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Background: Having experienced more than a year of pandemic, a variety of applications such as online classrooms, virtual office meetings, conferences, online games, Social media & Networks, Mobile applications, and many other infotainment areas have made humans live with gadgets and respond to them. However, all these applications have an impact on human behavioral transformation. It is very significant for employers to understand the emotions of their employees in the era of online office & work from home concept to increase productivity. Learning and identifying emotions from the human face has its application in all online portals when physical contact could not be achieved. Ojbective: Human Facial emotions can be learned using enormous feature descriptors that extract image features. While local feature descriptors retrieve pixel-level information, global feature descriptors extract the overall image information. Both of the feature descriptors quantify the image information, however, they don’t provide complete and relevant information. Hence, this research work aims to improve the existing local feature descriptor to perform globally for emotion recognition. Method: Our proposed feature descriptor, Patch-SIFT collects features from multiple patches within an image. This strategy is evolved to globally apply the local feature descriptor as a hybridization paradigm. The extracted features are trained and tested on an ensemble model. Findings: The Proposed Feature descriptor (Patch-SIFT) performance with ensemble model is found to produce an improved accuracy of 98% compared with existing feature descriptors and Machine learning classifiers. Novelty: This research work tries to evolve a new Feature descriptor algorithm based on SIFT algorithm for an efficient emotion recognition system that works without the need for any additional GPU or huge dataset.

Keywords

Classification, Ensemble, Feature descriptor, PatchSIFT

References

Hema D, Kannan S, . Performance study of Prevalent Feature Descriptors for Object detection. International Journal of Computer Engineering and Applications. 2018;p. 294–301. Available from: http://www.ijcea.com/wp-content/uploads/2018/02/NCDCM_2017_paper_49.pdf
Gupta S, Kumar M, Garg A. Improved object recognition results using SIFT and ORB feature detector. Multimedia Tools and Applications. 2019;78(23):34157–34171. Available from: https://dx.doi.org/10.1007/s11042-019-08232-6
Wang W, Xu K, Niu H, Miao X. Emotion Recognition of Students Based on Facial Expressions in Online Education Based on the Perspective of Computer Simulation. Complexity. 2020;p. 1–9. Available from: https://dx.doi.org/10.1155/2020/4065207
Shabat AMM, Tapamo J. Angled local directional pattern for texture analysis with an application to facial expression recognition. IET Computer Vision. 2018;12(5):603–608. Available from: https://dx.doi.org/10.1049/iet-cvi.2017.0340
Talele K, Tuckley K. Facial expression recognition using digital signature feature descriptor. Signal, Image and Video Processing. 2020;14(4):701–709. Available from: https://dx.doi.org/10.1007/s11760-019-01595-1
wu S, Wang D. Effect of subject's age and gender on face recognition results. Journal of Visual Communication and Image Representation. 2019;60:116–122. Available from: https://dx.doi.org/10.1016/j.jvcir.2019.01.013
Ergin S, Isik S, Gulmezoglu MB. Face Recognition by Using 2D Orthogonal Subspace Projections. Traitement du Signal. 2021;38(1):51–60. Available from: https://dx.doi.org/10.18280/ts.380105
Niu B, Gao Z, Guo B. Facial Expression Recognition with LBP and ORB Features. Computational Intelligence and Neuroscience. 2021;p. 1–10. Available from: https://dx.doi.org/10.1155/2021/8828245
Meng H, Yuan F, Wu Y, Yan T. Facial Expression Recognition Algorithm Based on Fusion of Transformed Multilevel Features and Improved Weighted Voting SVM. Mathematical Problems in Engineering. 2021;p. 1–17. Available from: https://dx.doi.org/10.1155/2021/6639598
Jeong D, Kim BG, Dong SY. Deep Joint Spatiotemporal Network (DJSTN) for Efficient Facial Expression Recognition. Sensors. 2020;20. Available from: https://doi.org/10.3390/s20071936
Tian Y, Cheng J, Li Y, Wang S. Secondary Information Aware Facial Expression Recognition. IEEE Signal Processing Letters. 2019;26(12):1753–1757. Available from: 10.1109/LSP.2019.2942138
Kim JH, Kim BG, Roy PP, Jeong DM. Efficient Facial Expression Recognition Algorithm Based on Hierarchical Deep Neural Network Structure. IEEE Access. 2019;7:41273–41285. Available from: 10.1109/ACCESS.2019.2907327
Koujan 1MR, Alharbawee L, Giannakakis G, Pugeault N, Roussos A. Anastasios Roussos, “Real-time Facial Expression Recognition “In The Wild” by Disentangling 3D Expression from Identity”. 15th IEEE International Conference on Automatic Face and Gesture Recognition. 2020. Available from: https://arxiv.org/pdf/2005.05509.pdf
Jeong D, Kim J, Lee Y, Kim B. Robust Weighted Keypoint Matching Algorithm for Image Retrieval. Proceedings of the 2018 the 2nd International Conference on Video and Image Processing. 2018. Available from: https://doi.org/10.1145/3301506.3301513
Chhabra P, Garg NK, Kumar M. Content-based image retrieval system using ORB and SIFT features. Neural Computing & Applications. 2020;32:2725–2733. Available from: https://doi.org/10.1007/s00521-018-3677-9
Kumar M, Chhabra P, Garg &NK. An Efficient Content Based Image Retrieval System using BayesNet and K-NN. Multimedia Tools and Applications. 2018. Available from: 10.1007/s11042-017-5587-8
Kumar A, Kaur A, Kumar M. Face detection techniques: a review. Artificial Intelligence Review. 2019;52(2):927–948. Available from: https://dx.doi.org/10.1007/s10462-018-9650-2
Kumar M, Bansal M. 2D object recognition: a comparative analysis of SIFT, SURF and ORB feature descriptors. Multimedia Tools and Applications. 2021. Available from: 10.1007/s11042-021-10646-0
Monika, Kumar M. XGBoost: 2D-Object Recognition Using Shape Descriptors and Extreme Gradient Boosting Classifier. International conference on Computational Methods and Data Engineering. 2021. Available from: 10.1007/978-981-15-6876-3_16
Singh S, Ahuja U, Kumar M. Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment. Multimedia Tools and Applications. 2021;80:19753–19768 . Available from: https://doi.org/10.1007/s11042-021-10711-8
Hema D, Kannan S. Hybridizing Local and Global Features by Sequential Fusion Technique for Object Detection and Learning. International Journal of Advanced Science and Technology. 2020;29(05):2401–2407. Available from: http://sersc.org/journals/index.php/IJAST/article/view/11023

Copyright

© 2021 Hema & Kannan.This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)