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Author

Ting Tang

Bio: Ting Tang is an academic researcher. The author has contributed to research in topics: Fingerprint recognition & Region of interest. The author has an hindex of 1, co-authored 1 publications receiving 15 citations.

Papers
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Proceedings ArticleDOI
29 May 2012
TL;DR: An image-based fingerprint recognition method by using wavelet transformation and this method is efficient even for low quality fingerprint, achieved on the FVC2002 database.
Abstract: Image-based and minutiae-based are two major methods of fingerprint recognition. In this work, we presented an image-based fingerprint recognition method by using wavelet transformation and this method is efficient even for low quality fingerprint. The features extraction of the proposed method differing with previous wavelet methods is based on the blocks of enhanced region of interest (ROI). The alignment is required to build ROI including location the reference point and rotation alignment. Fingerprint matching was performed on simply Euclidian distance of feature vector extracted from wavelet domain. These features consist of mean energy, standard deviation and Shannon entropy for the purpose of making these features more discriminative. The good recognition accuracy was achieved on the FVC2002 database.

16 citations


Cited by
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Journal ArticleDOI
TL;DR: A novel fingerprint feature extraction method based on the Curvelet transform to reduce the dimensionality of the fingerprint image and to improve the verification rate and the use of the possibility theory as a global framework to build a possibility fingerprint knowledge basis to be exploited in order to make a fingerprint verification decision.
Abstract: A fingerprint feature extraction step represents the success key of the fingerprint verification process. In a matching step, the good processing of those features would generate a measure that reflects more accurately the similarity degree between the input fingerprint and the template. In our study, we propose a novel fingerprint feature extraction method based on the Curvelet transform to reduce the dimensionality of the fingerprint image and to improve the verification rate. Like all extractors, the features which are generated by the Curvelet transform are usually imprecise and reflect an uncertain representation. Therefore, we proposed to analyze these features by a possibility theory to deal with imprecise and uncertain aspect in our novel fingerprint matching method. Thus, this paper focused on presenting a novel fingerprint features extraction method and a novel matching method. The features extraction method consists of two main steps: decompose the fingerprint image into a set of sub-bands by the Curvelet transform and extract the most discriminative statistical features of these sub-bands. A possibility based representation of those statistical features would be achieved by a possibility theory. So, the proposed fingerprint matching method is based on the use of the possibility theory as a global framework, including knowledge representation (as a possibility measure); in order to build a possibility fingerprint knowledge basis to be exploited in order to make a fingerprint verification decision. An extensive experimental evaluation shows that the proposed fingerprint verification approach is effective in terms of fingerprint image representation and possibility verification reasoning.

18 citations

Journal ArticleDOI
01 Aug 2020
TL;DR: The overall performance of the proposed multimodal biometric systems is better than that of the unimodal systems based on different classifiers and different fusion levels and rules.
Abstract: Multimodal biometric system can be accomplished at different levels of fusion and achieve higher recognition performance than the unimodal system. This paper concerned to study the performance of different classification techniques and fusion rules in the context of unimodal and multimodal biometric systems based on the electrocardiogram (ECG) and fingerprint. The experiments are conducted on ECG and fingerprint databases to evaluate the performance of the proposed biometric systems. MIT-BIH database is utilized for ECG, FVC2004 database is utilized for the fingerprint, and further experiments are being performed to evaluate the proposed multimodal system with 47 subjects from virtual multimodal database. The performance of the proposed unimodal and multimodal biometric systems is measured using receiver operating characteristic (ROC) curve, AUC (area under the ROC curve), sensitivity, specificity, efficiency, standard error of the mean, and likelihood ratio. The findings indicate AUC up to 0.985 for sequential multimodal system, and up to 0.956 for parallel multimodal system, as compared to the unimodal systems that achieved AUC up to 0.951, and 0.866, for the ECG and fingerprint biometrics, respectively. The overall performance of the proposed multimodal systems is better than that of the unimodal systems based on different classifiers and different fusion levels and rules.

14 citations

Proceedings ArticleDOI
03 Sep 2015
TL;DR: Comparison of all the four transform is presented here and it is observed that DCT and DFT gives better result as compared to DWT and Gabor filter.
Abstract: Fingerprint Recognition is one of the oldest and popular methods for person identification. There are two major approaches of fingerprint recognition namely image based and minutiae based. This paper presents an image based fingerprint recognition method. In this work first core point is detected and pre processing of image is done then transformation technique like Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Gabor Filter are used for feature extraction. These features consist of mean energy, standard deviation and Shannon entropy. The good recognition accuracy is achieved on the FVC 2002 database. Comparison of all the four transform is presented here and it is observed that DCT and DFT gives better result as compared to DWT and Gabor filter.

14 citations

Journal ArticleDOI
17 Jun 2016
TL;DR: It is noted that edge detection is one of the most fundamental processes within the low level vision and provides the basis for the higher level visual intelligence in primates, and its implementation needs a cross-disciplinary approach in neuroscience, computing and pattern recognition.
Abstract: This review provides an overview of the literature on the edge detection methods for pattern recognition that inspire from the understanding of human vision. We note that edge detection is one of the most fundamental processes within the low level vision and provides the basis for the higher level visual intelligence in primates. The recognition of the patterns within the images relates closely to the spatiotemporal processes of edge formations, and its implementation needs a cross-disciplinary approach in neuroscience, computing and pattern recognition. In this review, the edge detectors are grouped in as edge features, gradients and sketch models, and some example applications are provided for reference. We note a significant increase in the amount of published research in the last decade that utilises edge features in a wide range of problems in computer vision and image understanding having a direct implication to pattern recognition with images.

9 citations

Proceedings ArticleDOI
01 Dec 2015
TL;DR: This partial fingerprint recognition system is intended to support forensic science particularly during crime scene investigations where full images of the fingerprints are impossible to locate.
Abstract: Traditional pattern recognition systems were implemented using neural network systems in order to recognize partial prints ranging from 100%, 75%, 50%, 40% and 30% of the whole fingerprint image. The uniqueness of the patterns of each individual fingerprint served as the motivation in pursuing this research in identifying partial fingerprints where the minutiae pattern identification method cannot be used due to incomplete information arising from the partiality of the fingerprint. Promising results are achieved during experimentations. This system is also able to identify partial prints subjected to incorporated noise and at most 10% blank spots within the prints. This partial fingerprint recognition system is intended to support forensic science particularly during crime scene investigations where full images of the fingerprints are impossible to locate.

6 citations