A Novel Approach for Deep Learning Models Based on Web Applications With The Effective Use of Proposed Virtual Assistant

Article Sidebar

pdf
Published: Sep 29, 2023
DOI: https://doi.org/10.53555/jrtdd.v6i1.2613
Keywords:
Emotion Recognition System Virtual Assistant Machine Learning Feature Extraction Sentimental  Analysis

Main Article Content

Dr. Rishi Kumar Sharma
Dr. Nitesh Kaushik
Sunil Kushwaha
Mr. Siddharth Jain
Bhanu Partap

Abstract

Human being expresses himself in terms of gestures that could be hand gestures, facial expression, or text grammar. The emotion recognition system is the Human- Computer in- teraction medium the way a computer program can understand the human feelings.This study recognizes the facial expression of the person by feature detection algorithm. The proposed method uses Deep learning models of Python library that is functions of Deep analyze library to classify the facial expression into namely seven categories: Happy, Sad, Angry, Disgust, Neutral, Fear and Surprise. The purpose of this study is to identify the patterns of emotion changes from the images throughout the video capturing and create a dashboard and analyze the actual trend in the emotion change. The system tends to then understand the emotion via. Chat with the person and then give a result based on the summation of both the  trends.

Article Details

How to Cite
Dr. Rishi Kumar Sharma, Dr. Nitesh Kaushik, Sunil Kushwaha, Mr. Siddharth Jain, & Bhanu Partap. (2023). A Novel Approach for Deep Learning Models Based on Web Applications With The Effective Use of Proposed Virtual Assistant. Journal for ReAttach Therapy and Developmental Diversities, 6(1), 1061–1067. https://doi.org/10.53555/jrtdd.v6i1.2613
Issue
Vol. 6 No. 1 (2023)
Section
Articles
Author Biographies

Dr. Rishi Kumar Sharma

Associate Professor, CSE Quantum University, Roorkee, India 

Dr. Nitesh Kaushik

Professor, CSE,Anand International College of  Engineering, Jaipur, India 

Sunil Kushwaha

Assistant Professor, FCE, Poornima University, Jaipur, India

Mr. Siddharth Jain

Assistant Professor , CSE, Anand International College of  Engineering, Jaipur, India

Bhanu Partap

Assistant Professor, CSE, Quantum University, Roorkee, India

References

Roddy Cowie, Ellen Douglas-Cowie, Nicolas Tsapatsoulis, George Vot- sis, Stefanos Kollias, Winfried Fellenz, and John G Taylor. Emotion recognition in human-computer interaction. IEEE Signal processing magazine, 18(1):32– 80, 2001.

Alvin I Goldman and Chandra Sekhar Sripada. Simulationist models of face-based emotion recognition. Cognition, 94(3):193–213, 2005.

Moataz El Ayadi, Mohamed S Kamel, and Fakhri Karray. Survey on speech emotion recognition: Features, classification schemes, and databases. Pattern recognition, 44(3):572–587, 2011.

Suraj Tripathi, Abhay Kumar, Abhiram Ramesh, Chirag Singh, and Promod Yenigalla. Deep learning-based emotion recognition system us- ing speech features and transcriptions. arXiv preprint arXiv:1906.05681, 2019.

C. Loconsole, D. Chiaradia, V. Bevilacqua, A. Frisoli Real-time emotion recognition: an improved hybrid approach for classification performance Intelligent Computing Theory (2014), pp. 320-331.

S. Vassilis, Ju¨rgen Herrmann Where do machine learning and human- computer interaction meet? (1997) [7] 7. P. Ekman, W.V. Friesen, S. Ancoli Facial signs of emotional experi- ence J Pers Soc Psychol, 39 (1980), pp. 1123-1134.

Michael J. Lyons, Shigeru Akemastu, Miyuki Kamachi, Jiro Gyoba. Coding Facial Expressions with Gabor Wavelets, 3rd IEEE International Conference on Automatic Face and Gesture Recognition, pp. 200-205 (1998)

Carcagn`ı P, Del Coco M, Leo M, Distante C. Facial expression recog- nition and histograms of oriented gradients: a comprehensive study. SpringerPlus. 4:645. (2015)