Emotion Recognition from Facial Expressions using GFE, LBP And Hog Feature Extraction Techniques
PrVishal D. Bharate1, Devendra S. Chaudhari2, Mayur D. Chaudhari3
1Vishal D. Bharate, Department of Electronics and Telecommunication, Government College of Engineering, Amravati Sinhgad Academy of Engineering, Pune, India.
2Devendra S. Chaudhari, Department of Electronics and Telecommunication, Government College of Engineering, Jalgaon, Jalgaon, India.
3Mayur D. Chaudhari, Data Architect, Parkar Labs, Pune, India.
Manuscript received on September 12, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 2835-2840 | Volume-8 Issue-12, October 2019. | Retrieval Number: L30301081219/2019©BEIESP | DOI: 10.35940/ijitee.L3030.1081219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The social interaction of human beings is many times influenced by non-verbal communication, especially facial expressions. In societal life face of a human being is mostly observed by surrounding people to know the inner feelings. Thus, face forms a significant source of expressing human emotions, typically categorized into surprise, anger, fear, disgust, sad and happy. In the variety of behavioral science fields, emotion recognition has a significant role to play. The present paper describes a system in which preprocessing is performed by median filtering. Before extracting features, the watershed segmentation is applied to get the required characteristics of an image. In this paper, the Gamma based Feature Extraction (GFE), Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) technique have been used for feature extraction. The LBP algorithm is additionally tested with and without application of gamma correction using GFE. Two classifiers, namely kNN and SVM, have been employed, and their performance is compared. kNN and SVM, being supervised classifiers, can aid in better accuracy with proper training.
Keywords: Median Filtering, Watershed Segmentation, GFE, LBP, HOG, kNN and SVM
Scope of the Article: Pattern Recognition