Facial Feature Based Secure Information Transmission

2017 ◽  
Author(s):  
Chen Li ◽  
Yanjie Wang ◽  
Simon Fong ◽  
Chenhui Duan ◽  
Wei Song
Keyword(s):  
Information Transmission ◽  
Facial Feature ◽  
Secure Information ◽  
Feature Based

Formal Specification of Information Transmission Architecture Based on Automatic Generation of Primitives in Digital Campus

2013 ◽  
Vol 846-847 ◽  
pp. 1644-1647
Author(s):  
Xiao Le Li ◽  
Ying Wen ◽  
Ming Weng
Keyword(s):  
Information Transmission ◽  
Formal Analysis ◽  
Automatic Generation ◽  
Comprehensive Analysis ◽  
Security Requirements ◽  
Digital Campus ◽  
Application Systems ◽  
Secure Information ◽  
Common Framework ◽  
Automatic Tool

Based on comprehensive analysis on security requirements of information transmission, security primitive is generated by automatic tool in asymmetric key cryptosystem, and improved with addition of compositional factors. And then, formal processes of secure information transmission are constructed with composition method. Formal analysis shows that, secrecy, integrity, availability, controllability, non-repudiation and identifiability during information transmission can be insured by this architecture, as a common framework for development of various application systems in digital campus from the viewpoint of information security.

scholarly journals Deep-Facial Feature-Based Person Reidentification for Authentication in Surveillance Applications

2019 ◽  
Author(s):  
Yogameena Balasubramanian ◽  
Nagavani Chandrasekaran ◽  
Sangeetha Asokan ◽  
Saravana Sri Subramanian
Keyword(s):  
Facial Feature ◽  
Feature Based ◽  
Surveillance Applications

Forensic video solution using facial feature‐based synoptic Video Footage Record

2016 ◽  
Vol 10 (4) ◽  
pp. 315-322 ◽  
Author(s):  
Yogameena Balasubramanian ◽  
Kokila Sivasankaran ◽  
Sindhu Priya Krishraj
Keyword(s):  
Facial Feature ◽  
Video Footage ◽  
Feature Based

scholarly journals Deep Learning for Facial Beauty Prediction

Information ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 391
Author(s):  
Kerang Cao ◽  
Kwang-nam Choi ◽  
Hoekyung Jung ◽  
Lini Duan
Keyword(s):  
Deep Learning ◽  
Information Transmission ◽  
Network Design ◽  
Gradient Flow ◽  
Facial Feature ◽  
Feature Representation ◽  
Feature Maps ◽  
Regression Problem ◽  
The Arts ◽  
Facial Beauty

Facial beauty prediction (FBP) is a burgeoning issue for attractiveness evaluation, which aims to make assessment consistent with human opinion. Since FBP is a regression problem, to handle this issue, there are data-driven methods for finding the relations between facial features and beauty assessment. Recently, deep learning methods have shown its amazing capacity for feature representation and analysis. Convolutional neural networks (CNNs) have shown tremendous performance on facial recognition and comprehension, which are proved as an effective method for facial feature exploration. Lately, there are well-designed networks with efficient structures investigated for better representation performance. However, these designs concentrate on the effective block but do not build an efficient information transmission pathway, which led to a sub-optimal capacity for feature representation. Furthermore, these works cannot find the inherent correlations of feature maps, which also limits the performance. In this paper, an elaborate network design for FBP issue is proposed for better performance. A residual-in-residual (RIR) structure is introduced to the network for passing the gradient flow deeper, and building a better pathway for information transmission. By applying the RIR structure, a deeper network can be established for better feature representation. Besides the RIR network design, an attention mechanism is introduced to exploit the inner correlations among features. We investigate a joint spatial-wise and channel-wise attention (SCA) block to distribute the importance among features, which finds a better representation for facial information. Experimental results show our proposed network can predict facial beauty closer to a human’s assessment than state-of-the-arts.

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