Input images in iris recognition systems: A case study

2011 ◽  
Author(s):  
Inmaculada Tomeo-Reyes ◽  
Judith Liu-Jimenez ◽  
Ivan Rubio-Polo ◽  
Jorge Redondo-Justo ◽  
Raul Sanchez-Reillo
Keyword(s):  
Iris Recognition ◽  
Recognition Systems

Age dependency of the diabetes effects on the iris recognition systems performance evaluation results

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mohammadreza Azimi ◽  
Seyed Ahmad Rasoulinejad ◽  
Andrzej Pacut
Keyword(s):  
Iris Recognition ◽  
Recognition Task ◽  
Aging Effect ◽  
Recognition System ◽  
Health Condition ◽  
Age Dependency ◽  
Biometric Systems ◽  
Kolmogorov Smirnov ◽  
Recognition Systems ◽  
Smirnov Test

AbstractIn this paper, we attempt to answer the questions whether iris recognition task under the influence of diabetes would be more difficult and whether the effects of diabetes and individuals’ age are uncorrelated. We hypothesized that the health condition of volunteers plays an important role in the performance of the iris recognition system. To confirm the obtained results, we reported the distribution of usable area in each subgroup to have a more comprehensive analysis of diabetes effects. There is no conducted study to investigate for which age group (young or old) the diabetes effect is more acute on the biometric results. For this purpose, we created a new database containing 1,906 samples from 509 eyes. We applied the weighted adaptive Hough ellipsopolar transform technique and contrast-adjusted Hough transform for segmentation of iris texture, along with three different encoding algorithms. To test the hypothesis related to physiological aging effect, Welches’s t-test and Kolmogorov–Smirnov test have been used to study the age-dependency of diabetes mellitus influence on the reliability of our chosen iris recognition system. Our results give some general hints related to age effect on performance of biometric systems for people with diabetes.

Approximate Computing for Iris Recognition Systems

2018 ◽  
pp. 331-348 ◽  
Author(s):  
Hokchhay Tann ◽  
Soheil Hashemi ◽  
Francesco Buttafuoco ◽  
Sherief Reda
Keyword(s):  
Iris Recognition ◽  
Approximate Computing ◽  
Recognition Systems

A review of information fusion techniques employed in iris recognition systems

2012 ◽  
Vol 4 (3/4) ◽  
pp. 211 ◽  
Author(s):  
Petru Radu ◽  
Konstantinos Sirlantzis ◽  
Gareth Howells ◽  
Farzin Deravi ◽  
Sanaul Hoque
Keyword(s):  
Information Fusion ◽  
Iris Recognition ◽  
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.

Speech Technologies for Language Therapy

2011 ◽  
pp. 408-433
Author(s):  
Ingrid Kirschning ◽  
Ronald Cole
Keyword(s):  
Markov Models ◽  
Lessons Learned ◽  
Learning Tools ◽  
Speech And Language ◽  
Language Therapy ◽  
Effective Interactions ◽  
Speech And Language Therapy ◽  
Recognition Systems ◽  
Special Education School

This chapter presents the development and use of speech technologies in language therapy for children with hearing disabilities. It describes the challenges that must be addressed to design and construct a system to support effective interactions. The chapter begins with an introduction to speech and language therapy and discusses how speech-based systems can provide useful tools for speech and language therapy and to overcome the lack of sufficient human resources to help all children who require it. Then it describes the construction of adequate speech recognition systems for children, using artificial neural networks and hidden Markov models. Next, a case study is presented with the system we have been developing for speech and language therapy for children in a special education school. The chapter concludes with an analysis of the obtained results and the lessons learned from our experiences that will hopefully inform and encourage other researchers, developers, and educators to develop learning tools for individuals with disabilities.

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