Real-time online fingerprint image classification using adaptive hybrid techniques

This paper presents three different hybrid classification techniques applied for the first time in real-time online fingerprint classification. Classification of online real time fingerprints is a complex task as it involves adaptation and tuning of classifier parameters for better classification accuracy. To accomplish the optimal adaptation of parameters of functional link artificial neural network (FLANN) for real-time online fingerprint classification, proven and established optimizers, such as Biogeography based optimizer (BBO), Genetic algorithm (GA), and Particle swarm optimizer (PSO) are intelligently infused with it to form hybrid classifiers. The global features of the real-time fingerprints are extracted using a Gabor filter-bank and then passed into adaptive hybrid classifiers for the desired classification as per the Henry system. Three hybrid classifiers, the optimized weight adapted Biogeography based optimized functional link artificial neural network (BBO-FLANN), Genetic algorithm based functional link artificial neural network (GA-FLANN) and Particle swarm optimized functional link artificial neural network (PSO-FLANN), are explored for real-time online fingerprint classification, where the PSO-FLANN technique  is showing superior performance as compared to GA-FLANN and BBO-FLANN techniques. The best accuracy observed by the application of PSO-FLANN, is 98% for real-time online fingerprint classification

A new enhancement of fingerprint classification for the damaged fingerprint with adaptive features

In this paper, we propose an new enhancement of the classification for damaged fingerprint database.It is based on the fact that damaged fingerprint image is composed of regular texture regions that can be successfully represents by co-occurrence matrices.So, we first extract the features based on certain characteristics and then we use these features to train a neural network for classifying fingerprints into five classes.The obtained results compared with existing approaches demonstrate the superior performance of our new enhancement

[[alternative]]A Study of Real-Time Fingerprint Identificaton System Using DSP

計畫編號：NSC91-2213-E032-018研究期間：200208~200307研究經費：552,000[[sponsorship]]行政院國家科學委員

Does EigenPalm work? A system and evaluation perspective

Author name used in this publication: Adams KongAuthor name used in this publication: David ZhangRefereed conference paper2006-2007 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Fingerprint center point location using directional field

This paper presents a reliable fingerprint center point (CP) location algorithm for the alignment of fingerprints to construct a shift invariant fingerprint recognition system. The proposed algorithm is based on Alteration Tracking (AT) and CP estimation (CPE). AT is proposed to extract a track that records the transition from one quantized direction to another. CPE is aimed to find the bending point with highest transition of direction from the transition track. This algorithm is tested against fingerprints captured from SAGEM MSO100 optical scanner and the second database from University of Bologna. Experimental result shows that the proposed algorithm is capable of reliably locating fingerprint CP

A study on the use of Gabor features for Chinese OCR

The authors revisit the topic of Gabor feature extraction for Chinese OCR. We adopt a very simple discriminant function to construct a maximum discriminant function based character recognizer. We experiment with a simple way of forming a feature vector for each character image by extracting Gabor features using one wavelength at locations uniformly sampled with one spatial resolution. Extensive experiments on large vocabulary Chinese OCR for both machine-printed and handwritten characters are performed by using a large amount of training and testing data to demonstrate the effectiveness of the Gabor features for Chinese OCR. Using Gabor features as raw features, we have constructed several state-of-the-art Chinese OCR engines.published_or_final_versio