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

Improved 3D AAMs for automatic location of facial feature based on CIS 3D facial imaging

2009 ◽  
Vol 2009 (5) ◽  
pp. 74-78
Author(s):  
Jianguo Jiang ◽  
Hong Chang ◽  
Shu Zhan ◽  
Ando S.
Keyword(s):  
Facial Feature ◽  
Automatic Location ◽  
Feature Based

Automated In-Radius Authenticated Attendance System

2020 ◽  
Vol 17 (8) ◽  
pp. 3349-3354
Author(s):  
K. Ashok Kumar ◽  
Velagala Tarun Kumar Reddy ◽  
Vemula Shanmukha Srinivas
Keyword(s):  
Higher Education ◽  
Computational Intelligence ◽  
International Workshop ◽  
Near Field ◽  
Facial Feature ◽  
Near Field Communication ◽  
Student Attendance ◽  
Smart Classroom ◽  
Classroom Effects ◽  
Feature Based

Attendance is the most time-consuming task though there exist alternatives a specified device is essential to mark attendance some of them are NFC (Mohd Ameer Hakim bin Mohd Nasir and Muhammad Hazimuddin bin Asmuni, 2015. A review of student attendance system using near-field communication (NFC) technology; Ichimura, T. and Kamada, S., 2013. Early Discovery of Chronic Non-Attenders by Using NFC Attendance Management System. IEEE 6th International Workshop on Computational Intelligence and Applications, Hiroshima, IEEE. pp.191–196; FernAndez, M.J.L., et al., 2013. Control of attendance applied in higher education through mobile NFC technologies. Expert Systems with Applications, 40(1), pp.4478–4489; Shen C.W., et al., 2014. Developing a NFC-equipped smart classroom: Effects on attitudes toward computer science. Computers in Human Behavior, 30, pp.731–738.) based attendance, facial feature-based attendance, RFID based attendance, biometric based attendance. Each attendance system mentioned above have its own disadvantages and spamming techniques which makes students to make themselves present though they are not. So, we came up with an idea to overcome all those defects and complete the task within seconds.

scholarly journals Tracking Subtle Stereotypes of Children with Trisomy 21: From Facial-Feature-Based to Implicit Stereotyping

PLoS ONE ◽  
2012 ◽  
Vol 7 (4) ◽  
pp. e34369 ◽  
Author(s):  
Claire Enea-Drapeau ◽  
Michèle Carlier ◽  
Pascal Huguet
Keyword(s):  
Trisomy 21 ◽  
Facial Feature ◽  
Feature Based ◽  
Implicit Stereotyping
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