Latent Fingerprint Matching: A Survey

Sankaran, Anush; Vatsa, Mayank; Singh, Richa

doi:10.1109/access.2014.2349879

Cited by 69 publications

(32 citation statements)

References 74 publications

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“…Latent fingerprint matching has been still an open problem in the field of fingerprint recognition because of its difficulty. There is a good survey paper for latent fingerprint matching 30) . For more details, please refer to this paper.…”

Section: Latent Fingerprint Recognitionmentioning

confidence: 99%

[Invited Paper] Recent Advances in Biometric Recognition

Ito

Aoki

2018

MTA

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This paper presents recent advances in biometric recognition, where we focus on face, fingerprint and iris recognition, which are major research topics on biometric recognition. We summarize the research trend of face, fingerprint and iris recognition over the past decade. This paper also presents our activities of biometric recognition. Our approach employs the phase information obtained by Discrete Fourier Transform (DFT) of images. The phase information preserves the inherent features of the image, and its correlation function, called phase correlation or Phase-Only Correlation (POC), gives us both the good similarity measure for biometric recognition and the translational displacement for image registration.Our approach of using phase information has been successfully applied to fingerprint, face, iris, palmprint, finger knuckle and dental recognition. Among them, we present some interesting results of palmprint recognition, finger knuckle recognition and dental recognition.

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Section: Latent Fingerprint Recognitionmentioning

confidence: 99%

[Invited Paper] Recent Advances in Biometric Recognition

Ito

Aoki

2018

MTA

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“…Fingerprint detection and analysis is one of the most common and important techniques for criminal investigation used by law enforcement agencies for over 100 years [1]. Fingerprints have the most distinctive patterns in comparison to other publicly available biometric traits, such as Face, Iris etc.…”

Section: Introductionmentioning

confidence: 99%

“…These records are maintained by police or National Crime Record bureau. Latent fingerprint identification is performed with the help of ACE-V technique (a semi-automatic approach) [1], [16].…”

Section: Introductionmentioning

confidence: 99%

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Latent Fingerprint preprocessing: Orientation field correction using region wise dictionary

Kumar

Velusamy

2015

2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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Latent Fingerprint Images have been extensively used by law enforcement agencies in investigating the crime spot and use the necessary information obtained as evidence to validate the criminal in Court. Although an important breakthrough in this direction has already been made in plain biometrics recognition, still identifying biometric such as Face in CCTV footage and Latent Fingerprint in uncontrolled, uncooperative, and hostile environment is an open research problem. Poor quality, lack of clarity, absence of proper mechanism make the latent fingerprint preprocessing problem one of the persistent and challenging problem to extract the reliable features. Dictionary based learning technique has given significant result, in contrast to conventional orientation field estimation methods by reconstructing orientation field to enhance the latent fingerprint image. Distorted orientation field is corrected using orientation patches of good quality fingerprint from region wise dictionary. This paper proposes a fresh idea to construct the dictionary by region wise to correct the orientation field in latent image. To verify the accuracy of enhanced image, a statistical observation has been done and got the promising results. This study concentrates on latent fingerprint preprocessing module towards reliable and efficient (optimal) Latent Fingerprint Identification.

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“…The foreground area (or region of interest) contains the relevant information for a fingerprint comparison. Segmentation is still a challenging problem for latent fingerprints [33] which are very low-quality fingerprints lifted from crime scenes. Both the foreground and background area can contain "noise" on all scales, from small objects or dirt on the surface to written or printed characters (on paper) and largescale objects like an arc drawn by the forensic examiner.…”

Section: Feature Extractionmentioning

confidence: 99%

Directional global three-part image decomposition

Thai

Gottschlich

2016

J Image Video Proc.

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We consider the task of image decomposition, and we introduce a new model coined directional global three-part decomposition (DG3PD) for solving it. As key ingredients of the DG3PD model, we introduce a discrete multi-directional total variation norm and a discrete multi-directional G-norm. Using these novel norms, the proposed discrete DG3PD model can decompose an image into two or three parts. Existing models for image decomposition Advantages of the DG3PD model over existing ones lie in the properties enforced on the cartoon and texture images. The geometric objects in the cartoon image have a very smooth surface and sharp edges. The texture image yields oscillating patterns on a defined scale which are both smooth and sparse. Moreover, the DG3PD method achieves the goal of perfect reconstruction by summation of all components better than the other considered methods. Relevant applications of DG3PD are a novel way of image compression as well as feature extraction for applications such as latent fingerprint processing and optical character recognition.

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Latent Fingerprint Matching: A Survey

Cited by 69 publications

References 74 publications

[Invited Paper] Recent Advances in Biometric Recognition

[Invited Paper] Recent Advances in Biometric Recognition

Latent Fingerprint preprocessing: Orientation field correction using region wise dictionary

Directional global three-part image decomposition

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