Open AccessRETRACTED: Multi Facial Expression Recognition (MFER) for Identifying Customer Satisfaction on Products using Deep CNN and Haar Cascade Classifier
Author(s) -
D. N. V. S. L. S. Indira,
L. Sumalatha,
Babu Rao Markapudi
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1074/1/012033
Subject(s) - computer science , facial expression , facial expression recognition , classifier (uml) , expression (computer science) , artificial intelligence , cascade , feeling , pattern recognition (psychology) , machine learning , facial recognition system , speech recognition , psychology , engineering , social psychology , chemical engineering , programming language
Face Expression is one of the most normal, remarkable and a general sign for individuals to convey on their enthusiastic states and it is not restricted to national borders, linguistics and gender. This article presents the modeling of a framework that plans to foresee the fulfillment of a customer through his facial feelings. The cutting edge innovation of Facial Expression Recognition framework is the consumer satisfaction estimation. MFER, a Novel procedure is proposed in this paper for identifying consumer satisfaction levels. This sound methodology of client satisfaction estimation is an alternative option of the ordinary method of gathering clients’ reaction. This model must anticipate client’s behavior in the dynamic cycle. To expect consumer trustworthiness, we have characterized mathematical highlights of the face by utilizing Deep CNN and Haar Cascade Classifier. The kinds of consumer fulfillment are classified as satisfied, not-satisfied and neutral. Our framework shows a decent exhibition, testing it on the FER2013 dataset. Our MFER –Multi Facial Expression Recognition procedure identifies multiple objects in the same image which consists of same and different expressions.