Analysis of Facial Emotion Recognition with Various Techniques

Garima Sethi; Krishan Kant Sharma; Mohit Yadav; Khushwant Singh; Fernando  Moreira

doi:10.15575/join.v11i1.1674

Authors

Garima Sethi Department of Mathematics, University Institute of Sciences, Chandigarh University, Mohali-140413 Punjab, India https://orcid.org/0000-0001-8410-8454
Krishan Kant Sharma Department of Mechanical Engineering, Chandigarh University, Mohali,140413, Punjab, India https://orcid.org/0000-0002-3830-8145
Mohit Yadav Department of Mathematics, University Institute of Sciences, Chandigarh University, Mohali-140413 Punjab, India https://orcid.org/0000-0002-9332-8480
Khushwant Singh Department of Computer Science and Engineering, Maharshi Dayanand University, Rohtak, India https://orcid.org/0000-0001-6732-055X
Fernando Moreira REMIT, IJP, Universidade Portucalense, Porto & IEETA, Universidade de Aveiro, Aveiro and IEETA, Aveiro University, Aveiro, Portugal https://orcid.org/0000-0002-0816-1445

DOI:

https://doi.org/10.15575/join.v11i1.1674

Keywords:

Convolutional Neural Network, Facial Emotion Recognition, Image Acquisition, Prediction Accuracy

Abstract

Facial emotion recognition (FER) is a prominent investigation area in computer vision and affective computing. It involves the automatic detection and analysis of human emotions based on facial expressions. The current work offers a broad analysis of the present state-of-the-art approaches, methodologies, and challenges in facial emotion recognition. The paper explores the various components involved in FER, including face detection, feature extraction, classification algorithms, and datasets. Additionally, it discusses the applications, limitations, and future directions of FER research. The aim of this research is to utilize Facial Emotion Recognition (FER) as an advancing technique with considerable ramifications across multiple sectors. Contemporary facial emotion recognition (FER) research extensively employs deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). To enhance the performance of the FER system, attempt various feature extraction strategies, model designs, and hyper-parameter setups. Advancements in deep learning and computer vision techniques have considerably enhanced the precision and efficacy of FER systems, allowing for the accurate detection and classification of emotions from facial expressions. Facial Emotion Recognition has advanced considerably in the precise identification and interpretation of emotions conveyed through facial expressions. Ongoing research and innovation in FER could transform multiple fields, including human-computer interface, healthcare diagnostics, market research, and beyond.

Author Biographies

Garima Sethi, Department of Mathematics, University Institute of Sciences, Chandigarh University, Mohali-140413 Punjab

Assistant Professor

Krishan Kant Sharma, Department of Mechanical Engineering, Chandigarh University, Mohali,140413, Punjab

Assistant Professor

Mohit Yadav, Department of Mathematics, University Institute of Sciences, Chandigarh University, Mohali-140413 Punjab

Assistant Professor

Fernando Moreira, REMIT, IJP, Universidade Portucalense, Porto & IEETA, Universidade de Aveiro, Aveiro and IEETA, Aveiro University, Aveiro

Professor

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