Information technology. A study of the differential impact of demographic factors in biometric recognition system performance

2021 ◽  
Keyword(s):  
Information Technology ◽  
System Performance ◽  
Demographic Factors ◽  
Recognition System ◽  
Biometric Recognition ◽  
Differential Impact

KINERJA iLEARNING RAHARJA ASK AND NEWS (IRAN) DALAM MENDUKUNG KEGIATAN TRIDHARMA IDUHELP!

CCIT Journal ◽  
2014 ◽  
Vol 8 (1) ◽  
pp. 18-33
Author(s):  
Untung Rahardja ◽  
Meta Amalya Dewi ◽  
Fitri Lisnawati
Keyword(s):  
Information Technology ◽  
System Performance ◽  
Communication Media ◽  
Related Method ◽  
Literature Reviews ◽  
The World ◽  
Information And Communication ◽  
System Requirements ◽  
Three Stages ◽  
Care System

Advances in information technology and communications which we achieve now actually been recognized and felt in the world of education in general. Currently College Prog implement a Tridharma terms Tridharma iDuHelp!. Tridharma is one of the basic responsibilities that students must be developed simultaneously and together. In this Tridharma still there are problems in the system iDuHelp! service. So IRAN (iLearning Prog Ask and News) in collaboration with iDuHelp! in providing answers and information needed by the student. In its application in Tridharma iDuHelp! IRAN There is a related method in it, such as iLearning methods that are currently being developed. With iLearning method can facilitate conduct research in detail, accurately, and clearly by using mindmapping. Besides the method of analysis is also done with three stages  namely the identification of the problem, identifying needs, and identifying system requirements. In this study using 4 literature reviews that can be used as references in preparing this paper. In this article explained about the problems that arise and solving problems in accurately using the flow Flowchart. In the implementation of the prototype shown iDuHelp! As well as the performance of Iran. So the end result of the study is a system performance to information and communication media of Iran can maximize iDuHelp! care system  It is widely integrated in a university.

Multimodal score level fusion for recognition using face and palmprint

2020 ◽  
pp. 002072092092966
Author(s):  
Milind E Rane ◽  
Umesh S Bhadade
Keyword(s):  
Recognition System ◽  
Acceptance Rate ◽  
Face Database ◽  
Biometric Recognition ◽  
False Acceptance Rate ◽  
Score Level Fusion ◽  
Score Fusion ◽  
Matching Score ◽  
Level Fusion ◽  
Fusion Approach

The paper proposes a t-norm-based matching score fusion approach for a multimodal heterogenous biometric recognition system. Two trait-based multimodal recognition system is developed by using biometrics traits like palmprint and face. First, palmprint and face are pre-processed, extracted features and calculated matching score of each trait using correlation coefficient and combine matching scores using t-norm based score level fusion. Face database like Face 94, Face 95, Face 96, FERET, FRGC and palmprint database like IITD are operated for training and testing of algorithm. The results of experimentation show that the proposed algorithm provides the Genuine Acceptance Rate (GAR) of 99.7% at False Acceptance Rate (FAR) of 0.1% and GAR of 99.2% at FAR of 0.01% significantly improves the accuracy of a biometric recognition system. The proposed algorithm provides the 0.53% more accuracy at FAR of 0.1% and 2.77% more accuracy at FAR of 0.01%, when compared to existing works.

scholarly journals Enhancing iris recognition system performance using templates fusion

2012 ◽  
Vol 3 (2) ◽  
pp. 133-140 ◽  
Author(s):  
Aly I. Desoky ◽  
Hesham A. Ali ◽  
Nahla B. Abdel-Hamid
Keyword(s):  
System Performance ◽  
Iris Recognition ◽  
Recognition System

The X factor that can help to motivate Generation Y

2015 ◽  
Vol 23 (1) ◽  
pp. 32-34 ◽  
Author(s):  
S.S. Sreejith
Keyword(s):  
Information Technology ◽  
Performance Evaluation ◽  
Evaluation System ◽  
Evaluation Criteria ◽  
Recognition System ◽  
Information Technology Workers ◽  
Content Type ◽  
Performance Evaluation System ◽  
Evaluation Systems ◽  
Effective Performance

Purpose – Explains why performance evaluation designed for manufacturers is inappropriate for information technology organizations. Design/methodology/approach – Underlines the distinctiveness of the information technology workforce and provides the basis for an effective performance- evaluation system designed for these workers. Findings – Highlights the roles of consensus and transparency in setting and modifying evaluation criteria. Practical implications – Urges the need for a fair and open rewards and recognition system to run in parallel with reformed performance evaluation. Social implications – Provides a way of updating performance evaluation systems to take account of the move from manufacturing to information technology-based jobs in many developed and developing societies. Originality/value – Reveals how best to recognize, reward and assess the performance of information technology workers.

scholarly journals CAPSULE NETWORK BASED BIOMETRIC RECOGNITION SYSTEM

2019 ◽  
Vol 2019 (2) ◽  
pp. 83-94 ◽  
Author(s):  
Dr. I. Jeena Jacob
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Recognition System ◽  
Noise Removal ◽  
Personal Identification ◽  
Biometric Recognition ◽  
Learning Techniques ◽  
Frame Work ◽  
The Stability

The biometric recognition plays a significant and a unique part in the applications that are based on the personal identification. This is because of the stability, irreplaceability and the uniqueness that is found in the biometric traits of the humans. Currently the deep learning techniques that are capable of strongly generalizing and automatically learning, with the enhanced accuracy is utilized for the biometric recognition to develop an efficient biometric system. But the poor noise removal abilities and the accuracy degradation caused due to the very small disturbances has made the conventional means of the deep learning that utilizes the convolutional neural network incompatible for the biometric recognition. So the capsule neural network replaces the CNN due to its high accuracy in the recognition and the classification, due to its learning capacities and the ability to be trained with the limited number of samples compared to the CNN (convolutional neural network). The frame work put forward in the paper utilizes the capsule network with the fuzzified image enhancement for the retina based biometric recognition as it is a highly secure and reliable basis of person identification as it is layered behind the eye and cannot be counterfeited. The method was tested with the dataset of face 95 database and the CASIA-Iris-Thousand, and was found to be 99% accurate with the error rate convergence of 0.3% to .5%

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