Forensic applications like criminal investigations, terrorist identification and National security issues require a strong fingerprint data base and efficient identification system. In this paper we propose DWT based Fingerprint Recognition using Non Minutiae (DWTFR) algorithm. Fingerprint image is decomposed into multi resolution sub bands of LL, LH, HL and HH by applying 3 level DWT. The Dominant local orientation angle θ and Coherence are computed on LL band only. The Centre Area Features and Edge Parameters are determined on each DWT level by considering all four sub bands. The comparison of test fingerprint with database fingerprint is decided based on the Euclidean Distance of all the features. It is observed that the values of FAR, FRR and TSR are improved compared to the existing algorithm.

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DWT Based Fingerprint Recognition using Non Minutiae Features

Fingerprint recognition refers to the automated method of verifying a match between two human fingerprints which is used to identify individuals and verify their identity. A fingerprint sensor is used to capture a digital image of the fingerprint pattern. The captured image is digitally processed to create a biometric template (a collection of extracted features) which is stored and used for matching. In this paper, we investigate a fingerprint recognition approach by local robust features extraction and matching. In this approach, first the local features are extracted using Speeded-Up Robust Feature (SURF) algorithm. Then the features of the test fingerprint image are compared against two or more exiting template image features for matching. The matching method uses a matching threshold. Two features match when the distance between them is less than the matching threshold. It also eliminates ambiguous matches in addition to using the matching threshold. Finally it calculates the similarity index/matching score from the matching points and take the decision on matching. Since SURF is a scale and rotation invariant algorithm, the fingerprint recognition system shows better recognition accuracy in presence of rotation, scaling and partial distortion of the test image. The experimental results indicate its effectiveness and improved performance over the state of the art.

Speeded-Up Robust Feature extraction and matching for fingerprint recognition

(Aim) Unilateral sensorineural hearing loss is a brain disease, which causes slight morphology within brain structure. Traditional manual method can ignore this change. (Method) First, we used dual-tree complex wavelet transform to extract features. Afterwards, we used kernel principal component analysis to reduce feature dimensionalities. Finally, multinomial logistic regression was employed to be the classifier. (Result) The 10 times of 10-fold stratified cross validation showed our method achieved an overall accuracy of 96.17 \(\mathrm {\pm }\) 2.49%. The sensitivities of detecting left-sided sensorineural hearing loss, right-sided sensorineural hearing loss, and healthy controls were 96.00 \(\mathrm {\pm }\) 2.58%, 96.50 \(\mathrm {\pm }\) 2.42%, and 96.00 \(\mathrm {\pm }\) 3.16%, respectively. (Conclusion) Our method performed better than five state-of-the-art methods.

Preliminary Study on Unilateral Sensorineural Hearing Loss Identification via Dual-Tree Complex Wavelet Transform and Multinomial Logistic Regression

Contrary to popular belief, despite decades of research in fingerprints, reliable fingerprint recognition from large database is an open problem. Extracting features out of poor quality prints is the most challenging problem faced in this area. For that we need effective and efficient fingerprint matching algorithms that meet user requirements, to identify similarity. This paper gives a brief survey of current fingerprint matching methods and technical achievement in this area. The survey includes a large number of papers covering the research aspects of system design and applications of fingerprint matching, image feature representation and extraction. Furthermore future research directions are suggested.

Fingerprint Identification — Ideas, Influences, and Trends of New Age

A robust fingerprint recognition system based on marginal statistics of 2D wavelet transform is introduced which significantly improves the accuracy of previous wavelet based approaches due to 1) a better selection of features extracted from the wavelet transform, and 2) a more accurate distance measure to find the similarity between fingerprints. A combination of Jain and Poincare algorithms is employed to locate the fingerprint reference point. The main part of the fingerprint image is chosen as a rectangle with the reference point at its center. The image is then divided into nonoverlapping sub-images, the wavelet transform is applied to the bi-level sub-images, and Generalized Gaussian Density (GGD) features are extracted from each wavelet sub band. Finally, the fingerprint recognition is done through the k-Nearest Neighbor (k-NN) classification employing Kullback-Leibler Distance (KLD) measure. Our test results confirm the superiority of the proposed method over the current fingerprint recognition methods.

A Robust Wavelet-based Approach to Fingerprint Indentification

This paper presents an algorithm for fingerprint identification based on wavelet transform and Gabor features. Firstly, a center point area of the fingerprint is detected, then the image in this area is decomposed into different sub-images using wavelet transform, finally we extract Gabor features from these sub-images to generate feature vectors for matching. The experiment conducted over the four FVC2004 databases shows that the proposed approach can capture much texture information at different scales and orientations, achieve high recognition rates.

Cheng Xu

Papers

An Algorithm for Fingerprint Identification Based on Wavelet Transform and Gabor Feature