Effective Facial Emotion Recognition using Convolutional Neural Network Algorithm
MalyalaDivya1, R Obula Konda Reddy2, C Raghavendra3
1M Divya*, Department. of Computer Science Engineering, Institute of Aeronautical Engineering, Dundigal, Hyderabad, India.
2Dr. R Obula Konda Reddy*, Professor, Department. of Computer Science Engineering, Institute of Aeronautical Engineering, Dundigal, Hyderabad, India.
3C Raghavendra*,Asst. Professor, Department. of Computer Science Engineering, Institute of Aeronautical Engineering, Dundigal, Hyderabad, India.
Manuscript received on November 11, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on 30 November, 2019. | PP: 4351-4354 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8275118419/2019©BEIESP | DOI: 10.35940/ijrte.D8275.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Convolutional Neural Network, Face Detection, Face Detection, Feature Extraction, Image processing, Opencv, Tensor flow.
Scope of the Article: Artificial Intelligent Methods, Models, Techniques.