Fast Iris Detection for Personal Verification Using Modular Neural Nets
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
A Rotation Invariant Algorithm for Recognition
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Fast Face Detection Using Neural Networks and Image Decomposition
AMT '01 Proceedings of the 6th International Computer Science Conference on Active Media Technology
Fast Modular Neural Nets for Human Face Detection
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Biometric Recognition: Security and Privacy Concerns
IEEE Security and Privacy
Human iris detection using fast cooperative modular neural nets and image decomposition
Machine Graphics & Vision International Journal
Speeding-up normalized neural networks for face/object detection
Machine Graphics & Vision International Journal
Fast principal component analysis for face detection using cross-correlation and image decomposition
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
New fast principal component analysis for real-time face detection
Machine Graphics & Vision International Journal
Human face detection using new high speed modular neural networks
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
A sub-block-based eigenphases algorithm with optimum sub-block size
Knowledge-Based Systems
Hi-index | 0.00 |
With the evolution of information technology, our society is becoming more and more electronically connected. Daily transactions between individuals or between individuals and various organizations are conducted increasingly through highly interconnected electronic devices. The capability of automatically establishing the identity of individuals is thus essential to the reliability of these transactions. For many reasons, new techniques must be emitted in order to solve the problem of automatic personal identification. First, the current level of security does not match the specifications defined for the application. Second, fraud in the current application is too high and uncontrollable. Third, current verification methods are expensive and unreliable. Traditional personal identification approaches which use "something that you know" such as Personal Identification Number (PIN), or something that you have such as an Identification tag (ID card, like a padge for example) are not sufficiently reliable to satisfy the security requirements of electronic transactions because they lack the capability to differentiate between a genuine individual and an imposture who fraudulently acquires the access privilege. Biometric approaches of identification are enjoying a renewed interest. They refer to automatic recognition of individuals based on a feature vectors derived from their physiological like Face, Fingerprint and/or behavioral characteristic such as Signature. Biometric recognition systems should provide reliable personal recognition schemes to either confirm or determine the identity of an individual. By using biometrics a person could be identified based on "who she/he is" rather then "what she/he has" (card, token, key) or "what she/he knows" (password, PIN). In this paper, the study presented in [77] is extended. A brief overview of biometric methods, both unimodal and multimodal, as well as their advantages and disadvantages are presented. Furthermore, combined techniques for authentication are introduced. In addition, more attention for palm vein recognition is given. Vein pattern biometrics presents many advantages over outdated biometric methods. Vein-pattern biometric technologies require little physical contact. Furthermore, they are unique to each user In addition, they are fast and easy to use. Moreover, this pattern will not vary over the course of a person's lifetime.