Smart Facial Emotion Recognition with Gender and Age Factor Estimation

Title

Smart Facial Emotion Recognition with Gender and Age Factor Estimation

Creator

Teja Chavali S.; Tej Kandavalli C.; Sugash T.M.; Subramani R.

Description

Human-Computer Interaction (HCI) in an intelligent way, which aims at creating scalable and flexible solutions. Big tech firms and businesses believe in the success of HCI as it allows them to profit from on-demand technology and infrastructure for information-centric applications without having to use public clouds. Because of its capacity to imitate human coding abilities, facial expression recognition and software-based facial expression identification systems are crucial. This paper proposes a system of recognizing the emotional condition of humans, given a facial expression, and conveys two methods of predicting the age and gender factors from human faces. This research also aims in understanding the influences posed by gender and age of humans on their facial expressions. The model can currently detect 7 emotions based on the facial data of a person - (Anger, Disgust, Happy, Fear, Sad, Surprise, and Neutral state). The proposed system is divided into three segments: a.) Gender Detection b.) Age Detection c.) Emotion Recognition. The initial model is created using 2 algorithms - KNN, and SVM. We have also utilized the architectures of some of the deep learning models such as CNN and VGG - 16 pre-trained models (Transfer Learning). The evaluation metrics show the model performance regarding the accuracy of the Recognition system. Future enhancements of this work can include the deployment of the DL and ML model onto an android or a wearable device such as a smartphone or a watch for a real-time use case. 2022 Elsevier B.V.. All rights reserved.

Source

Procedia Computer Science, Vol-218, pp. 113-123.

Date

2022-01-01

Publisher

Elsevier B.V.

Subject

Age recognition; CNN; Emotion recognition; Gender; HCI; KNN; SVM; VGG - 16

Coverage

Teja Chavali S., Department of Computer Science and Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, Bengaluru, 56003, India; Tej Kandavalli C., Department of Computer Science and Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, Bengaluru, 56003, India; Sugash T.M., Department of Mathematics, CHRIST, Deemed to Be University, Bengaluru, 560029, India; Subramani R., Department of Mathematics, CHRIST, Deemed to Be University, Bengaluru, 560029, India

Rights

All Open Access; Gold Open Access

Relation

ISSN: 18770509

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1016/j.procs.2022.12.407

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163687390&doi=10.1016%2fj.procs.2022.12.407&partnerID=40&md5=5dbafaeb530809dff94994d9451408cc