scholarly journals Face recognition systems in monkey and human: are they the same thing?

2013 ◽  
Vol 5 ◽  
Author(s):  
Galit Yovel ◽  
Winrich A. Freiwald
Keyword(s):  
Face Recognition ◽  
Recognition Systems

scholarly journals PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Kanokmon Rujirakul ◽  
Chakchai So-In ◽  
Banchar Arnonkijpanich
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Expectation Maximization ◽  
High Speed ◽  
Expectation Maximization Algorithm ◽  
Parallel Architecture ◽  
Principal Component ◽  
Eigenvalue Decomposition ◽  
Computational Time ◽  
Recognition Systems

Principal component analysis or PCA has been traditionally used as one of the feature extraction techniques in face recognition systems yielding high accuracy when requiring a small number of features. However, the covariance matrix and eigenvalue decomposition stages cause high computational complexity, especially for a large database. Thus, this research presents an alternative approach utilizing an Expectation-Maximization algorithm to reduce the determinant matrix manipulation resulting in the reduction of the stages’ complexity. To improve the computational time, a novel parallel architecture was employed to utilize the benefits of parallelization of matrix computation during feature extraction and classification stages including parallel preprocessing, and their combinations, so-called a Parallel Expectation-Maximization PCA architecture. Comparing to a traditional PCA and its derivatives, the results indicate lower complexity with an insignificant difference in recognition precision leading to high speed face recognition systems, that is, the speed-up over nine and three times over PCA and Parallel PCA.

scholarly journals Automatic Attendance Management System under Unconstrained Video using Face Recognition

2020 ◽  
Vol 9 (10) ◽  
pp. 229-232
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Management System ◽  
Traditional Method ◽  
Surveillance Camera ◽  
Recognition Systems ◽  
End To End ◽  
Learning By Using

Attendance Management System under unconstrained video using face recognition technology has made a great variation from the traditional method of attendance marking system. This attendance management system has been developed under the domain of Deep Learning by using Face recognition. Automatic Attendance Management under unconstrained video using face recognition systems which automatically mark attendance by detecting end to end face from the frames obtained from live stream video of surveillance camera which placed in center of the classroom. From the recognized faces, it will be compared with stored images in database, then the attendance report will be generated and it also provides attendance reports to parents of the absentee’s student.

A Few Approaches To Face Detection In Face Recognition Systems

2002 ◽  
pp. 313-322
Author(s):  
Georgi Koukharev ◽  
Tomasz Ponikowski ◽  
Liming Chen
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Recognition Systems

Face Recognition in Unconstrained Environment

2016 ◽  
pp. 217-242
Author(s):  
Santosh Kumar ◽  
Ramesh Chand Pandey ◽  
Shrikant Tiwari ◽  
Sanjay Kumar Singh
Keyword(s):  
Face Recognition ◽  
Future Research ◽  
User Cooperation ◽  
Still Images ◽  
Comprehensive Review ◽  
Security Applications ◽  
Unconstrained Face Recognition ◽  
Recognition Systems ◽  
Unconstrained Face

Research emphasizes in face recognition has shifted towards recognition of human from both still images and videos which are captured in unconstrained imaging environments and without user cooperation. Due to confounding factors of pose, illumination, image quality, and expression, as well as occlusion and low resolution, current face recognition systems deployed in forensic and security applications operate in a semi-automatic manner. This book chapter presents a comprehensive review of face recognition approaches in unconstrained environment. The objective of this book chapter is to address issues, challenges and recent advancement in face recognition algorithms which may help novel researchers to do innovative research in unconstrained environment. Finally, this chapter provides the stepping stone for future research to unveil how biometrics approaches can be deployed in unconstrained face recognition systems.

Robust Design of Face Recognition Systems

2006 ◽  
pp. 96-105
Author(s):  
Sunjin Yu ◽  
Hyobin Lee ◽  
Jaihie Kim ◽  
Sangyoun Lee
Keyword(s):  
Face Recognition ◽  
Robust Design ◽  
Recognition Systems

scholarly journals Implementing CCTV-Based Attendance Taking Support System Using Deep Face Recognition: A Case Study at FPT Polytechnic College

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 307 ◽  
Author(s):  
Ngo Tung Son ◽  
Bui Ngoc Anh ◽  
Tran Quy Ban ◽  
Le Phuong Chi ◽  
Bui Dinh Chien ◽  
...  
Keyword(s):  
Face Recognition ◽  
Motion Blur ◽  
Security Monitoring ◽  
Flexible System ◽  
Lighting Conditions ◽  
Satisfactory Performance ◽  
Measurement Results ◽  
The Face ◽  
Recognition Systems

Face recognition (FR) has received considerable attention in the field of security, especially in the use of closed-circuit television (CCTV) cameras in security monitoring. Although significant advances in the field of computer vision are made, advanced face recognition systems provide satisfactory performance only in controlled conditions. They deteriorate significantly in the face of real-world scenarios such as lighting conditions, motion blur, camera resolution, etc. This article shows how we design, implement, and conduct the empirical comparisons of machine learning open libraries in building attendance taking (AT) support systems using indoor security cameras called ATSS. Our trial system was deployed to record the appearances of 120 students in five classes who study on the third floor of FPT Polytechnic College building. Our design allows for flexible system scaling, and it is not only usable for a school but a generic attendance system with CCTV. The measurement results show that the accuracy is suitable for many different environments.

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