Academia.eduAcademia.edu

Outline

Title
Abstract
Key Takeaways
Figures
Introduction
Complex Methods of Authentication
Conclusion and Comments
References
All Topics
Computer Science
Cybersecurity

Image analysis and pattern recognition in biometric technologies

Profile image of Kukharev GeorgyKukharev Georgy

1997, Proc. of the 4-rd Int. …

Abstract

Many biometric methods are closely connected with methods of pattern recognition and image analysis. The realization of a number of biometric technologies requires using the last achievements in this area. Some elements of technology based on some methods of image analysis are demonstrated by the example of iris person identification. From a position of organizing the educational process, laboratory works in the area of biometric technologies allow stimulating students' inquisitiveness in studying methods and algorithms for image processing and pattern recognition. each of these phenomena can be applied in future to identify a person, and these methods may replace instead the traditional ones such as fingerprinting, handwriting, signature verification etc.

Key takeaways
sparkles

AI

  1. Biometric technologies (BTs) integrate pattern recognition and image analysis for enhanced identity verification.
  2. The text emphasizes the educational value of BTs in stimulating students' interest in image processing techniques.
  3. BTs applications span various fields including finance, law enforcement, and immigration control.
  4. Iris and fingerprint identification methods demonstrate high stability and reliability over time compared to signatures.
  5. Emerging non-contact biometric methods are predicted to enhance user convenience and identification accuracy.
Figures (7)
Fig.3. Change of the signature of Napoleon after some historical events on an during 20 years
Fig.3. Change of the signature of Napoleon after some historical events on an during 20 years
investigated methods of image processing and patter recognition. 3.3. Fingerprint authentication
investigated methods of image processing and patter recognition. 3.3. Fingerprint authentication
Fig.7. The tamper-proof card combines BTs and traditional person identification approaches
Fig.7. The tamper-proof card combines BTs and traditional person identification approaches
TE TSO MCAT ENN EE ESR CANE NE SORE CARE CANZRMCAL PEER SAE ORD INS EEE EEE ER Chso BEEN ENE ERICA EIN WEE EINCARENLE devices store a code which relates to the location of the minutiae points on the fingerprint. It should be said some words on compression methods for biometric information. For instance, instead of storing an actual image of the fingerprint as the template, most verification devices store a code which relates to the location of the minutiae points on the fingerprint. The template cannot be used to recreate an image of the fingerprint so it is of no use to law enforcement agencies. the templates range in size from 25 to 2000 bytes so can be stored as a bar code, on a magnetic stripe card, smart card or on a central database connected to the owner by a PIN.
TE TSO MCAT ENN EE ESR CANE NE SORE CARE CANZRMCAL PEER SAE ORD INS EEE EEE ER Chso BEEN ENE ERICA EIN WEE EINCARENLE devices store a code which relates to the location of the minutiae points on the fingerprint. It should be said some words on compression methods for biometric information. For instance, instead of storing an actual image of the fingerprint as the template, most verification devices store a code which relates to the location of the minutiae points on the fingerprint. The template cannot be used to recreate an image of the fingerprint so it is of no use to law enforcement agencies. the templates range in size from 25 to 2000 bytes so can be stored as a bar code, on a magnetic stripe card, smart card or on a central database connected to the owner by a PIN.
As any other rather new area of human activity, BTs have not today a system approach to design systems to realize BTs technology. For example, techniques and criterion of a choice of particular BTs for the purposes of applications have not been developed. It stimulates interest of many experts to the given subject area. NCSA (National Computer Security Association, USA) will perform various biometric projects on behalf of its corporate members as well as CBDS (Commercial Biometric Developers Consortium, USA) members. NCSA is likely to do research on biometric product effectiveness, to develop methodology to test and certify biometric products. For the same time, large experience of designing AFIS has been accumulated. The most simple engineering solutions of AFIS are widely used in banking, financial and access control.
As any other rather new area of human activity, BTs have not today a system approach to design systems to realize BTs technology. For example, techniques and criterion of a choice of particular BTs for the purposes of applications have not been developed. It stimulates interest of many experts to the given subject area. NCSA (National Computer Security Association, USA) will perform various biometric projects on behalf of its corporate members as well as CBDS (Commercial Biometric Developers Consortium, USA) members. NCSA is likely to do research on biometric product effectiveness, to develop methodology to test and certify biometric products. For the same time, large experience of designing AFIS has been accumulated. The most simple engineering solutions of AFIS are widely used in banking, financial and access control.

Related papers

Recognition of Human Iris Patterns for Biometric Identification
IOSR Journals

Iris recognition is an efficient method for identification of persons. This paper provides a technique for the iris recognition. A biometric system provides automatic identification of individuals. Iris is an unique feature which is applicable for identification. Unlike face recognition or finger prints, iris recognition comes from randomly distributed features. It is most reliable and accurate method of identification. This paper proposes a personal identification using iris recognition system with the help of six major steps i.e. image acquisition,localization, Isolation, normalization, feature extraction and matching and also these six steps consists a numbers of minor steps to complete each step. The boundaries of the iris, as papillary and limbic boundary, are detected by using Canny Edge Detector. We can use masking technique to isolate the iris image form the given eye image, this isolated iris image is transformed from Cartesian to polar coordinate. Now finally extract the unique features (feature vector) of the iris after enhancing the iris image and then perform matching process on iris code using Hamming Distance for acceptance and rejectance process.

View PDFchevron_right
A Close Approach to Iris Recognition System
IOSR Journals

Iris recognition, a relatively new biometric technology, has great advantages such as variability, stability and security, thus it is the most promising for high security environments. Among its applications are border control in airports and harbors, access control in laboratories and factories, identification for Automatic Teller Machines and restricted access to police evidence rooms. There have been several implementations of security systems using biometric, especially for identification and verification cases. The term "biometrics" is derived from the Greek words bio (life) and metric (to measure). The pattern used in the biometric is the iris pattern in human eye. The iris pattern has been proved unique for each person. A literature review of the most prominent algorithms implemented in each stage is presented. This paper provides a review of major iris recognition researches.

View PDFchevron_right
Human Identification Using IRIS Recognition
International Journal of Scientific Research in Science, Engineering and Technology IJSRSET

Identification and verification of a person today is a common thing; which may include door-lock system and vehicle control or even at accessing bank accounts via ATM, etc. which is necessary for securing personal information. The conventional methods like ID card verification or signature does not provide perfection and reliability. Identification by biological features gets tremendous importance with the increasing of security systems in society. Various types of biometrics like face, finger, iris, retina, voice, palm print, ear and hand geometry, in all these characteristics, iris recognition gaining attention because iris of every person is unique, it never changes during human lifetime and highly protected against damage. This unique feature shows that iris can be good security measure. Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual's eyes, whose complex patterns are unique, stable, and can be seen from some distance. The iris recognition systems have recently shown very high accuracies in verifying an individual's identity. The function of the iris is to control the amount of light entering through the pupil, and this is done by the sphincter and the dilator muscles, which adjust the size of pupil. The complete iris recognition system can be split into four stages: Image acquisition, segmentation, encoding and matching.

View PDFchevron_right
Biometric Authentication- Person Identification using Iris Recognition
Ankur Jyoti Sarmah

Zenodo (CERN European Organization for Nuclear Research), 2022

Biometrics are used to offer person identification by measuring and analyzing people's unique physiological and behavioral characteristics. As demands are increasing on this authentication technique a number of biometric modalities have evolved and used like fingerprint reader, face identifier and iris scanner. The human eye also has features and patterns that are distinctive for recognition. Also, the low-cost equipment for utilizing this technique has made it the most preferable framework in security reasons. As a reliable biometric authentication method, it is considered as the most explicit. In this paper, a standard database namely CASIA v3 is used for taking iris database and different algorithms are used to recognize the targeted areas and the recognition process is performed. The algorithms we used are Morphology binary to remove the unwanted backgrounds; Circular Hough Transform to detect the iris; Gabor's filter to extract features of the human eye and KNN classifier to finally authenticate the query image. The images from the database are acquired and pre-processed before running through the algorithms.

View PDFchevron_right
2009 Iris Image Based Biometric Technology
Swanirbhar Majumder

A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual i.e. "something you are". Iris recognition is regarded as the most reliable and accurate biometric identification system available after retinal scan or any other multi modal biometric which in their own respect have different constraints. Most commercial iris recognition systems use patented algorithms developed by Daugman, and these algorithms are able to produce perfect recognition rates. We here are trying to develop a system of human recognition by iris image processing but in our own way using a database of 2000 iris images of 50 persons with 20 images of each eye provided by the University of Bath iris image processing group. Then implement a recognition process based on correlation of edge detected iris patterns and check for match & mismatch.

View PDFchevron_right
Image feature extraction for application of biometric identification of iris - a morphological approach
Joceli Mayer

16th Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2003), 2003

This paper presents a new approach based on morphological operators for application of biometric identification of individuals by segmentation and analysis of the iris. Algorithms based on morphological operators are developed to segment the iris region from the eye image and also to highlight chosen iris patterns. The extracted features are used to represent and characterize the iris. In order to properly extract the desired patterns, an algorithm is proposed to produce skeletons with unique paths among end-points and nodes. The representation obtained by the morphological processing is stored for identification purposes. To illustrate the efficiency of the morphological approach some results are presented. The proposed system was derived to present low complexity implementation and low storage requirements.

View PDFchevron_right
Automatic people identification on the basis of iris pattern image processing and preliminary analysis
Joan Cabestany

2002

People identification gains more importance in life and consequently more concern in science. This paper tries to present a simple way to solve the problem through a basic analysis of the human iris. Main predicaments and strategies to overcome them are outlined and a direction of future activities leading to a complete and simple identification system is pointed out

View PDFchevron_right
A Review on Biometric Technology
Kartik choudhary

International Journal of Advanced Research in Computer Science and Software Engineering, 2018

Biometric Technology has turned out to be a popular area of research in computer vision and one of the most successful applications for identifying humans by capturing and analysing the sole feature or characteristic of individual which is possessed by them and involves their Physical and Behavioral characteristics. For the individual validation and authentication the biometric system has this responsibility. Biometric Technology started from the fingerprints recognition and later on improvements were done in it to make it more secure which involves the face recognition and iris Recognition. Almost both of them are available and regarded as the accurate and reliable technology for biometric validation system. This review paper is all about Face recognition techniques in biometric locking system and Iris recognition technique of identification and the ways of making locking systems ways more efficient, full of ease, more secure, and far better than before so as to make locking or security stronger. It discusses about face recognition technique, its working and its application in different sector along with iris recognition, its working, its application.

View PDFchevron_right
Iris Recognition Methods:A Review
Mehran Emadi, Nahal Avazpour, Journal of Computer Science IJCSIS

A biometric system allows automatic detection of persons on the basis of some types of biological features that are unique in every human being there provides. To date biometric systems based on fingerprint, facial features, voice, hand geometry, handwriting, Iris, retina, and is presented to us in this review of the literature we have the identification of the Iris and the advantages and disadvantages of each are discussed.

View PDFchevron_right
Iris Biometrics Recognition System and Comparison of Biometrics Techniques
Zohaib Bukhari

International Journal of Applied Information Systems, 2014

This paper is totally based on iris recognition and biometrics comparison techniques. Author revealed the story of iris recognition and biometrics comparison and provided the step by step detail about iris biometrics recognition and also elaborated the use of iris recognition and mentioned the key role played by it in daily life.

View PDFchevron_right

References (37)

  1. Ablameyko S., and Shmerko V., Graohics Recognition and Handwriting Text Identification: Belarussian Results and International Co-Operation, Proc. 3rd Int.Conf.on Applications of Computer Systems - ACS'96, (J.Soldek Editor.), Technical University of Szczecin, Poland, 1997, pp.343-354
  2. Ammar M. Progress in Verification of Skillfully Simulated Handwritten Signatures, Pattern Recognition and Artificial Intelligence, vol. 5, 1993, pp. 337-351.
  3. Absaloms H., and Tomikawa T., A Study of Human Eyes' Detection by Window-pair Chasing, Trans. Inst. Electr. Eng. (Japan), vol.116 -C, no.9, 1966, pp.1015-1028
  4. American National Standards, Fingerprint Identification -Data Format for Information Interchange, N.Y., 1986 Biometric Digest TM (USA), Ed. William Rogers, 1996/1997
  5. Biometric Technology Today TM (UK), Ed. Emma Newham, 1995/1996
  6. Biometrics Report, 1996 (UK)
  7. Carlson J.J., and others, Fusion of Multiple, Coarse Features from the Face, Hand and Voice for Realable Human Identity Determination. Proc. SPIE (USA), vol.2755, 1996, pp.283-293
  8. Chapel C.E., Fingerprint -A Manual of Identification. N.Y., Coward McCann, 1971
  9. Cheushev V., Miller J., Thomson P., and Popel D.,The Mathematical Model of Signatures for Compact Storage and Operative Personal Identification in Bank Systems, In.: V.Shmerko and S.Yanushkevich (Eds.), Banking Information Systems, Technical University of Szczecin, Poland, 1997, Clergeau-Tournemire Stephanie, and Plamondon Rejean, Integration of Lexical and Syntactical Knowledge in a Handwriting-Recognition System, Mashine Vision and Applications, Springer- Verlag, 8, 1995, pp.249-259
  10. Cui Y., and Weng J.J., Hand Sign Recognition from Intensity Image Sequences with Complex Backgrounds, Proc.of the 2nd int. Conf. on Automatic Face and Gesture recognition, Killngton, USA, 1996, pp.259-264
  11. Diatlov O., and Bocharova O., Criminalistic Estimation of Possibilities of Ascertainment of Forgery of Documents, which are Produced by Means of Assembling, In.: V.Shmerko, and S.Yanushkevich (Eds.), Banking Information Systems, Technical University of Szczecin, Poland, 1997, pp.
  12. Ezzat T., and Poggio T., Facial Analysis and Synthesis using Image-Based Models, Proc.of the the 2nd Int. Conf. on Automatic Face and Gesture Recognition, Killngton, VT, USA, 1996, pp.116-121
  13. Foltyniewicz R., Automatic Face Recognition via Wavelets and Mathematical Morphology, Proc. of the 13th Int. Conf. on Pattern Recognition, vol.2,1966, pp.13-17
  14. Gordey D., Gotin A., Zavgorodnev S., Lopatenko O., and Revinsky V., Structural Organization and Operation Principles of the National Fingerprint Identification System (NAFIS) of Ministry of Internal Affairs, Republic of Belarus, In.: V.Shmerko, and S.Yanushkevich (Eds.), Banking Information Systems, Technical University of Szczecin, Poland, 1997, pp.
  15. Halici U., and Ongun G., Fingerprint Classification Through Self-Organizing Feature Maps Modified to Treat Uncertainties, Proc. IEEE (USA), vol.84, no.10, 1996, pp.1497-1512
  16. Herpers R., and others, Context Based Detection of Keypoints and Features in Eye Regions, Proc. of the 13th Int. Conf. on Pattern Recognition, vol.2,1966, pp.23-28
  17. Huang Kai, and Yan Hong, Off-Line Signature Verification by a Neural Network Classifier, Proc. of the 17th Australian Conf. on Neural Networks, Canberra, 1966, pp.190-194
  18. Kochergov E., Decision Making System for Handwriting Person Identification Based on Neural Network, This issue
  19. Kovalev V., Diakova N., and Bondar Y. On Feature Selection for Face Image Recognition. In this book Kukharev G., Direct and Inverse Transformations of Spectra from Cartesian into Polar System of Coordinates, This issue
  20. Lee L. L., Berger T., and Aviczer E., Reliable On-Line Human Signature Verification Systems, IEEE Trans, on Pattern Analysis and Machine Intelligence (USA), vol.18, no.6, 1996, pp.643-647
  21. Maio D., and Maltoni D., Direct Gray-Scale Minutiae Detection in Fingerprints, IEEE Trans. on Pattern Analysis and Mashine Intelligence, vol.19, no.1, 1997, pp.27-40
  22. Mechtre B.M., Fingerprint Image Analysis for Automatic Identification, Mashine Vision and Applications, vol.6, no.2-3, 1993, pp.124-139
  23. Morgenshtein I. Psycho graphology, St.Petersburg, Publishing House by Vejerman, 1903 (In Russian)
  24. Penagos J.D., Prabhakaran N., and Wunnava S.V., An Efficient Scheme for Dynamic Signature Verification, IEEE SOUTHEASTCON'96, Tampa, FL, USA, 1996, pp.451-457
  25. Phillips Phil, and Yanushkevich S., Modern Conceptions of Information Security In.: V.Shmerko, and S.Yanushkevich (Eds.), Banking Information Systems, Technical University of Szczecin, Poland, 1997
  26. Plamondon R., and Lorette G., Automatic Signature Verification and Writer Identification -The State of the Art, Pattern Recognition, 1989, vol.22, no.2, pp.107-131
  27. Polonnikov R., and Korotkov K. (Eds.) Biometric Informatics and Eniology, St. Petersburg, 'Olga' Publishing House,1995 (In Russian)
  28. Saborian R., Genest G., and Preteux F., Pattern Spectrum as a Local Shape for Off-Line Signature Verification, Proc. of the 13th Int. Conf. on Pattern Recognition, vol.3,1966, pp.43-48
  29. Shmerko V., and Yanushkevich S. (Eds.), Banking Information Systems, Technical University of Szczecin, Poland, 1997
  30. Shmerko V., Experience of Designing and Using Signature and Handwriting Person Identification Systems, The BCS Computer Security Specialist Group Annual Conference 9-10th March, London, UK, 1995, British Computer Society, London UK
  31. Shmerko V., Kochergov E., Mikhaylenko S., and others, Systems for Personal Identification using Methods of Signature and Handwriting Verification. Proc. of the 3-rd Int. Conf. on Pattern Recognition and Information Analysis (S. Ablameyko Ed.), Minsk, Belarus, vol. 3, 1995, pp.25-35.
  32. Tee E.R., and Selvanathan N., Enahancing the Personal Identification Number Input as a Means of Identification Signature. Malaysian J.Comput. Sci. (Malaysia), vol.9, no.1, 1996, pp.37-41
  33. Xuhua Yang, and others, Selection of Features for Signature Verification Using the Genetic Algorithm, Comp. Ind.Eng. (UK), vol.30, no.4, 1996, pp.1037-1045
  34. Yaser Yacoob, and Larry S.Davis, Recognition Human Facial Expressions from Long Image Sequences Using Optical Flow. IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.18, no.6, 1996, pp.636-642
  35. Yokoo Y., and Hagiwara M., Human Faces Detection Method Using Genetic Algorithm, Proc. of 1966 IEEE Int.Conf. on Evolutionary Computation (Japan), 1996, pp.113-118
  36. Zavgorodnev S., Kolyada A., Revinsky V., and Selyaninov M., Methodical and Algorithmic Principles of Base Technology of Fingerprints and Imprints Processing for National Fingerprint Identification System (NAFIS) of Republic of Belarus, In.: V.Shmerko and S.Yanushkevich (Eds.), Banking Information Systems, Technical University of Szczecin, Poland, 1997
  37. Zhou R.W., and Quck C., An Automatic Fuzzy Neural Network Driven Signature Verification System, The IEEE Int. Conf. on Neural Networks, Washngton, USA, vol.2, 1996, pp.1034-1039

Related papers

Technology a Review on Various Iris Recognition Techniques in Biometrics
Emerson Williams

2015

The iris recognition is a kind of the biometrics technologies based on the physiological characteristics of human body, compared with the feature recognition based on the fingerprint, palm print, face and sound etc, the iris has some advantages such as uniqueness, stability, high recognition rate, and non infringing etc. The iris recognition consists of iris localization, normalization, encoding and comparison. Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques. In this paper we have presented a review on various techniques in iris recognition.

View PDFchevron_right
Biometric Recognition Techniques An Analysis
saima shoro

2017

Biometrics refers to innovations for measuring and investigating a person's physiological or behavioral qualities which are remarkable to people subsequently can be utilized to recognize a man. Biometrics is a developing technology which is utilized as a part of different fields like legal sciences, security system and secured territory. These are utilized as a part of different applications like mobiles, protectedentreein a building and ATM.Biometrics are intended to upgrade the safety and diminish defenselessness. The undertaken study aims to present the distinctive biometrics techniques i.e. Fingerprint recognition, Hand geometry recognition, Iris recognition,Face Recognition and Voice recognition which are accessible to implement a biometric system.This study will be helpful for the scholars, researchers and academicians

View PDFchevron_right
Biometric Identification Based on Iris Recognition
Hanaa Fathi Mahmood

JOURNAL OF EDUCATION AND SCIENCE, 1970

View PDFchevron_right
A Review of Iris Recognition System used for feature recognition and different techniques
Sadhna K Mishra

2013

The biometric person authentication technique based on the pattern of the human iris is well suited to be applied to any access control system requiring a high level of security. Iris recognition is a proven, accurate means to identify people. Iris recognition system includes the preprocessing system, segmentation, features extraction and recognition. Especially we focus on image segmentation and statistical feature extraction for iris recognition process. The iris recognition system is highly depending on segmentation. A good number of identification systems based on behavioral characteristics (such as voice, handwriting, signature, speech, and keystroke) and physical characteristics (such as face, finger print iris etc) are being employed for identification of an individual. A compare has been made between these two techniques using FAR analysis, FRR analysis, memory requirement and algorithmic complexity.

View PDFchevron_right
A Survey: Study About Biometric Iris Detection Technique in Digital Image Processing
Punit Johari

International Journal of Advanced Research in Computer Science, 2017

ABSTRACT: A safe and secure space is significant for everyone in their life. Biometric approaches are adapted for the proof of identity of individuals are currently increasingly being used in routine tasks like security areas, medical checkups etc. Here a study on iris recognition system for biometric identification due to attraction of having almost zero probability of finding similar iris to another even in genetic identical twins. As it is an external organ and it remains same throughout the life, allures towards other means of biometric identity systems. In this paper, brief detailing about work done previously in field of iris recognition. As proceeding with iris recognition, steps of image acquisition, preprocessing of captured image, segmentation, feature extraction, matching need to be performed. Keywords: Iris recognition, segmentation, security, Biometric, Hough transform, counter measurement, IRIS, etc.

View PDFchevron_right

Related topics

  • Image Processing
  • Biometrics Authentication Techno...
  • Biometric Authentication
    • 580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved