Welcome to the InfoSci Platform
Reference Hub1
Optimization-Driven Kernel and Deep Convolutional Neural Network for Multi-View Face Video Super Resolution

Optimization-Driven Kernel and Deep Convolutional Neural Network for Multi-View Face Video Super Resolution

Amar B. Deshmukh, N. Usha Rani
Copyright: © 2020 |Volume: 12 |Issue: 3 |Pages: 19
ISSN: 1941-6210|EISSN: 1941-6229|EISBN13: 9781799805816|DOI: 10.4018/IJDCF.2020070106
Cite Article Cite Article

MLA

Deshmukh, Amar B., and N. Usha Rani. "Optimization-Driven Kernel and Deep Convolutional Neural Network for Multi-View Face Video Super Resolution." IJDCF vol.12, no.3 2020: pp.77-95. http://doi.org/10.4018/IJDCF.2020070106

APA

Deshmukh, A. B. & Rani, N. U. (2020). Optimization-Driven Kernel and Deep Convolutional Neural Network for Multi-View Face Video Super Resolution. International Journal of Digital Crime and Forensics (IJDCF), 12(3), 77-95. http://doi.org/10.4018/IJDCF.2020070106

Chicago

Deshmukh, Amar B., and N. Usha Rani. "Optimization-Driven Kernel and Deep Convolutional Neural Network for Multi-View Face Video Super Resolution," International Journal of Digital Crime and Forensics (IJDCF) 12, no.3: 77-95. http://doi.org/10.4018/IJDCF.2020070106

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

One of the major challenges faced by video surveillance is recognition from low-resolution videos or person identification. Image enhancement methods play a significant role in enhancing the resolution of the video. This article introduces a technique for face super resolution based on a deep convolutional neural network (Deep CNN). At first, the video frames are extracted from the input video and the face detection is performed using the Viola-Jones algorithm. The detected face image and the scaling factors are fed into the Fractional-Grey Wolf Optimizer (FGWO)-based kernel weighted regression model and the proposed Deep CNN separately. Finally, the results obtained from both the techniques are integrated using a fuzzy logic system, offering a face image with enhanced resolution. Experimentation is carried out using the UCSD face video dataset, and the effectiveness of the proposed Deep CNN is checked depending on the block size and the upscaling factor values and is evaluated to be the best when compared to other existing techniques with an improved SDME value of 80.888.