A fingerprint classification technique using multilayer SOM (original) (raw)
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AN IMPROVED FINGERPRINT CLASSIFICATION TECHNIQUE
2nd International Conference on Electrical Engineering ICEENG, 1999
This paper presents an automatic fingerprint classification technique similar to that reported in [2] but, an inverse filtering technique was introduced to restore the distorted parts of the images prior to the feature extraction stage. The results have shown that introducing the inverse filter stage has improved the percentage of correct classification. It reaches 97.5% compared to the 95% correct classification obtained using the previously reported technique.
Support Vector Machine Based Fingerprint Identification
This work is released in biometric field and has as goal, development of a full automatic fingerprint identification system based on support vector machine. Promising Results of first experiences pushed us to develop codification and recognition algorithms which are specifically associated to this system. In this context, works were consecrated on algorithm developing of the original image processing, minutiae and singular points localization; Gabor filters coding and testing these algorithms on well known databases which are: FVC2004 databases & FingerCell database. Performance Evaluating has proved that SVM achieved a good recognition rate in comparing with results obtained using a classic neural network RBF.
Biometrics is the science and technology of measuring and analyzing biological data of human body, extracting a feature set from the acquired data, and comparing this set against to the template set in the database. Biometric techniques are gaining importance for personal authentication and identification as compared to the traditional authentication methods. Biometric templates are vulnerable to variety of attacks due to their inherent nature. When a person's biometric is compromised his identity is lost. In contrast to password, biometric is not revocable.
Performance of Fingerprint Enhancement and Classification Using Neural Network
INTERNATIONAL JOURNAL OF COMPUTER APPLICATION
Fingerprints are the best and most widely used form of biometric authentication. Everyone is known to have unique, immutable fingerprints. Fingerprints are secure to use because they do not change in one"s lifetime. Since fingerprints are unique, even between identical twins, they are perfect for various security uses. The goal of biometric authentication system is to compare two fingerprint images. The authentication in aadhaar card is done by this comparison. Human fingerprints are rich in details called minutiae, which can be used as identification marks for security purposes. Fingerprint authentication systems are based on local ridge features known as minutiae extraction, marking minutiae accurately and rejecting false ones. However, fingerprint images get degraded and corrupted due to variations in skin and impression conditions. To get accurate minutiae, image enhancement techniques are employed prior to minutiae extraction. This paper presents techniquesto verify the identity of fingerprint. So the minutiae points of fingerprint images are classified using support vector machine algorithm for the identification of type of fingerprints.
An Effective Fingerprint Verification Technique
Computing Research Repository, 2010
This paper presents an effective method for fingerprint verification based on a data mining technique called minutiae clustering and a graph-theoretic approach to analyze the process of fingerprint comparison to give a feature space representation of minutiae and to produce a lower bound on the number of detectably distinct fingerprints. The method also proving the invariance of each individual fingerprint by using both the topological behavior of the minutiae graph and also using a distance measure called Hausdorff distance.The method provides a graph based index generation mechanism of fingerprint biometric data. The self-organizing map neural network is also used for classifying the fingerprints.
Overview of Fingerprint Recognition System
— This article is an overview of a current research based on fingerprint recognition system. In this paper we highlighted on the previous studies of fingerprint recognition system. This paper is a brief review in the conceptual and structure of fingerprint recognition. The basic fingerprint recognition system consists of four stages: firstly, the sensor which is used for enrolment & recognition to capture the biometric data. Secondly, the pre-processing stage which is used to remove unwanted data and increase the clarity of ridge structure by using enhancement technique. Thirdly, feature extraction stage which take the input from the output of the pre-processing stage to extract the fingerprint features. Fourthly, the matching stage is to compare the acquired feature with the template in the database. Finally, the database which stores the features for the matching stags. The aim of this paper is to review various recently work on fingerprint recognition system and explain fingerprint recognition stages step by step and give summaries of fingerprint databases with characteristics.
Pattern recognition, 1996
A fingerprint classification algorithm is presented in this paper. Fingerprints are classified into five categories: arch, tented arch, left loop, right loop and whorl. The algorithm extracts singular points (cores and deltas) in a fingerprint image and performs classification based on the number and locations of the detected singular points. The classifier is invariant to rotation, translation and small amounts of scale changes. The classifier is rule-based, where the rules are generated independent of a given data set. The classifier was tested on 4000 images in the NIST-4 database and on 5400 images in the NIST-9 database. For he NIST-4 database, classification accuracies of 85.4% for the five-class problem and 91.1% for the four-class problem (with arch and tented arch placed in the same category) were achieved. Using a reject option, the four-class classification error can be reduced to less than 6% with 10% fingerprint images rejected. Similar classification performance was obtained on the NIST-9 database.
General Examination of Fingerprint Recognition Algorithms System
The whole process of fingerprint recognition could be divided into 5 main steps: Acquirement of fingerprint-The quality of acquired fingerprint is important for the fingerprint recognition. It is recommended to use a fingerprint sensor with a very good quality which could tolerate miscellaneous skin types, dumb humidity of the finger grain.
Characteristics and Application of the Fingerprint Recognition Systems
Fingerprint recognition systems functionality relay on the processing power of the underlying system that implements efficient algorithm for the fingerprint image analysis rather then on the image data acquisition principle efficiency. In this work basic method of the capacitive measurement principle used in most of the systems are analysed. Results lead to the conclusion that some system improvements are also possible through the modification of the existing method.
A survey on Fingerprint Biometric System Stages
This article is an overview of the basic architecture of the fingerprint system. We described the methodology of the Automated Fingerprint Identification System and the approaches and techniques used to obtain, analyze, match fingerprint, we also detailed the some techniques for feature extraction and matching stages; The paper divided into three parts; the first part is an introduction to the Automated Fingerprint Identification System and also provides a summarized description of fingerprint image preprocessing and a survey on some papers highlighted on fingerprint preprocessing techniques. The second part gives a detailed description of the fingerprint feature extraction technique regarding fingerprint identification and verification; the last part gives a description of the matching methods and approaches; we also provided a literature review of some of the methodologies and techniques used in the various stages of the Automated Fingerprint Identification system.