Open AccessEffective Facial Emotion Recognition using Convolutional Neural Network Algorithm
Author(s) -
M. Divya,
R Obula Konda Reddy*,
C. G. Raghavendra
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d8275.118419
Subject(s) - computer science , convolutional neural network , artificial intelligence , surprise , speech recognition , feature (linguistics) , pattern recognition (psychology) , disgust , emotion classification , pooling , facial recognition system , three dimensional face recognition , facial expression , face detection , feature extraction , face (sociological concept) , computer vision , psychology , social science , linguistics , philosophy , anger , psychiatry , sociology , social psychology
This paper presents the idea related to automated live facial emotion recognition through image processing and artificial intelligence (AI) techniques. It is a challenging task for a computer vision to recognize as same as humans through AI. Face detection plays a vital role in emotion recognition. Emotions are classified as happy, sad, disgust, angry, neutral, fear, and surprise. Other aspects such as speech, eye contact, frequency of the voice, and heartbeat are considered. Nowadays face recognition is more efficient and used for many real-time applications due to security purposes. We detect emotion by scanning (static) images or with the (dynamic) recording. Features extracting can be done like eyes, nose, and mouth for face detection. The convolutional neural network (CNN) algorithm follows steps as max-pooling (maximum feature extraction) and flattening.