Double OptconNet architecture based facial expression recognition in video processing

ABSTRACT

A Double optimization based convolution network model is introduced in the proposed video based facial expression recognition framework. The proposed model comprises U-shaped network, Residual-Network architecture, and Coot optimization. Before performing expression recognition, the input video is subjected to pre-processing, and face detection is performed over the extracted frames using the viola jones algorithm. The U-shaped network has the advantage of improving the processing speed of the convolution network, whereas the residual network can reduce the error that occurs during the frame encoding and gradient dissipation avoidance. Due to this merit, these two networks are combined and introduced in the proposed framework for facial expression recognition. The experimental evaluation is performed using a matrix laboratory tool over the three datasets: Affectiva-MIT Facial Expression Dataset, BAUM-1s and Real-world affective faces database. The comparative analysis shows that the proposed network has attained an efficient recognition rate than other existing network architecture.

KEYWORDS:

• Facial expression recognition (FER)
• double optimization based convolution network (Double optconnet)
• optimization
• database
• hyper parameter
• accuracy
• U-shaped network
• jones algorithm

Data availability statement

Data sharing is not applicable to this article.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Melam Nagaraju

Mr. Melam Nagaraju is an Assistant professor of Information Technology Department, Seshadrirao Gudlavalleru Engineering College, Gudlavalleru. He is pursuing Ph.D in Computer Science and Engineering at JNTUK, Kakinada, Andhra Pradesh. His areas of interest are Image & Video Processing, Machine Learning, Deep Learning, Mobile Computing and Internet of Things.

Adilakshmi Yannam

Dr. Adilakshmi Yannam is an Associate professor of Computer Science and Engineering Department, Seshadrirao Gudlavalleru Engineering College, Gudlavalleru. Her areas of interest are Security issues in Mobile Adhoc Networks, Artificial Intelligence and Machine Learning.

Siva Satya Sreedhar P

Mr. Siva Satya Sreedhar P is an Assistant Professor of Information Technology Department and he is pursuing Ph.D. in Computer Science and Engineering at Anna University, Chennai, Tamil Nadu. His areas of interest are Image Processing, Machine Learning, Deep Learning and Internet of Things.

Maridu Bhargavi

Mrs. Maridu Bhargavi is an Assistant professor of Computer Science and Engineering Department, Vignan University, Vadlamudi. Her areas of interest are Machine Learning and Deep Learning.