Secured Optimal Cost Approach for Bimodal Deep Face Recognition in IoT and Its Applications

View Sample PDF

Author(s): Madhavi Gudavalli (JNTUK–UCEN, India), Vidaysree P (Stanley Women's Engineering College, India), S Viswanadha Raju (JNTUH CEJ, India)and Surekha Borra (K.S. Institute of Technology, India)
Copyright: 2018
Pages: 13
Source title: Big Data Management and the Internet of Things for Improved Health Systems
Source Author(s)/Editor(s): Brojo Kishore Mishra (C. V. Raman College of Engineering, India)and Raghvendra Kumar (LNCT Group of Colleges, India)
DOI: 10.4018/978-1-5225-5222-2.ch010

Keywords: Computer Science & IT / Health Information Systems / Internet Technologies / Medical Information Science Reference

Purchase

View Secured Optimal Cost Approach for Bimodal Deep Face Recognition in IoT and Its Applications on the publisher's website for pricing and purchasing information.

Abstract

This chapter proposes an optimal cost security approach for the current and emerging trends in the Engineering centric IoT applications that offer an optimized infrastructure and human safety through bimodal deep face recognition. Human face determines the person identity that reveals information like age, gender, emotions, attractiveness and others. Face recognition attracted researchers to enhance its performance because of its potential usage in several commercial, law enforcement, government and video surveillance applications in which individuals perceive each other. In this chapter, authors propose a new secured optimal cost approach for deep face recognition based on feature level fusion of bi-features extracted through unsupervised deep learner, Autoencoder and Local Binary Patterns (LBP) respectively. The dimensionality of fused feature map is reduced and protected through Forward Error Correction (FEC) technique. An efficient optimal cost region matcher (OCRM) is accomplished with Canny edge detector to maximize the face recognition accuracy. OCRM uses north-west corner rule of the transportation problem that fulfills the Monge property. The experimental results demonstrate the superiority of the proposed face recognition system over unimodal systems (Autoencoder and LBP alone) when tested on ORL and Real face datasets with OCRM matcher which is interfaced through diverse IoT applications.

The IRMA Community

Research IRM

Secured Optimal Cost Approach for Bimodal Deep Face Recognition in IoT and Its Applications

Purchase

Abstract

Related Content

IRMA Sponsors