Automatic Fingerprint Classification Research Articles

A multi-classifier system for automatic fingerprint classification using transfer learning and majority voting

A multi-classifier system for automatic fingerprint classification using transfer learning and majority voting

Performance Analysis of Fingerprint Based Image Enhancement and Minutiae Extraction

Extracting minutiae from fingerprint images is one of the most important steps in automatic fingerprint identification and classification. Minutiae are local discontinuities in the fingerprint pattern, mainly terminations and bifurcations. In this work we have propose a method for fingerprint image enhancement. Using histogram equalization over filtering and then minutia are calculated. The results achieved are compared with those obtained through some other methods. The Results show some improvement in the minutiae extraction in terms of quantity.

A Review on Classification of Fingerprint Images

Fingerprint is most widely used in individual identification especially in civilian applications, forensic laboratories etc. due to large bio-invariant characteristics of human fingerprints. A search over all the records in a data base takes a very long time and deteriorates accuracy as database is very large. To avoid this inconvenience, automatic fingerprint classification method is employed that does not require any user's interaction, reduce the search space and time and also increases accuracy of identifications. Classifying fingerprint images is a very difficult pattern recognition problem, due to the small interclass variability, the large intraclass variability, and the presence of noise and the ambiguous properties of fingerprints. In this paper we discuss about fingerprint classification and also review existing methods that have been applied for the classification problem.

A Multi-Classifier Approach to Fingerprint Classification

Fingerprint classification is a challenging pattern recognition problem which plays a fundamental role in most of the large fingerprint-based identification systems. Due to the intrinsic class ambiguity and the difficulty of processing very low quality images (which constitute a significant proportion), automatic fingerprint classification performance is currently below operating requirements, and most of the classification work is still carried out manually or semi-automatically. This paper explores the advantages of combining the MASKS and MKL-based classifiers, which we have specifically designed for the fingerprint classification task. In particular, a combination at the ‘abstract level’ is proposed for exclusive classification, whereas a fusion at the ‘measurement level’ is introduced for continuous classification. The advantages of coupling these distinct techniques are well evident; in particular, in the case of exclusive classification, the FBI challenge, requiring a classification error ≤ 1% at 20% rejection, was met on NIST-DB14.

AN IMPROVED FINGERPRINT CLASSIFICATION TECHNIQUE

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.

Fingerprint classification by directional image partitioning

In this work, we introduce a new approach to automatic fingerprint classification. The directional image is partitioned into homogeneous connected regions according to the fingerprint topology, thus giving a synthetic representation which can be exploited as a basis for the classification. A set of dynamic masks, together with an optimization criterion, are used to guide the partitioning. The adaptation of the masks produces a numerical vector representing each fingerprint as a multidimensional point, which can be conceived as a continuous classification. Different search strategies are discussed to efficiently retrieve fingerprints both with continuous and exclusive classification. Experimental results have been given for the most commonly used fingerprint databases and the new method has been compared with other approaches known in the literature: As to fingerprint retrieval based on continuous classification, our method gives the best performance and exhibits a very high robustness.

Classification of fingerprint images using a neural network

AbstractFingerprints are an effective means of identifying individuals, and several fingerprint comparison systems have already been developed. However, since it becomes difficult to recognize fingerprints with ease as the number and diversity of fingerprint patterns increases, it becomes desirable to create an efficient database for fingerprint matching with the goal of developing an automatic fingerprint classification system. Here an automatic classification system is imposed for fingerprint images in which the discrimination system is a multilayer neural network composed of five subnetworks to carry out macrofeature extraction on the fingerprint image. A total of 50 training examples, 10 per category, and 500 testing samples were presented to the network. According to the obtained results, the network achieved an 86 percent classification rate for the first candidate and a 99 percent classification rate for the first two candidates. The state of the network was analyzed after training, and the reasons for the system effectiveness and its classification error were studied.

Structural descriptions of fingerprint images

The algorithm described is the first step of an automatic fingerprint classification system, whose motivation is to permit correlation studies between fingerprints and congenital diseases. It is concerned with the generation of stylized descriptions of fingerprints, obtained by applying linguistically oriented procedures.