Altered Fingerprints: Detection and Localization

Tabassi, Elham; Chugh, Tarang; Deb, Debayan; Jain, Anil K.

doi:10.1109/btas.2018.8698549

Cited by 12 publications

(18 citation statements)

References 12 publications

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“…An approach whereby proposed biometric system was modeled using a combination of two or more DL models were called ensemble DL approach. A study presented by [116] proposed a method for localization and detection of altered fingerprint in order to avoid obfuscation presentation attack. The first part of their model utilized CNN models (Inception-v3 and Mobilenet-v1) to detect and localize regions of fingerprint alterations.…”

Section: Hybrid Deep Learning Approach For Fingerprintmentioning

confidence: 99%

Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Chiroma¹

2021

AIS

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Deep Learning algorithms (DL) have been applied in different domains such as computer vision, image detection, robotics and speech processing, in most cases, DL demonstrated better performance than the conventional machine learning algorithms (shallow algorithms). The artificial intelligence research community has leveraged the robustness of the DL because of their ability to process large data size and handle variations in biometric data such as aging or expression problem. Particularly, DL research in automatic fingerprint recognition system (AFRS) is gaining momentum starting from the last decade in the area of fingerprint pre-processing, fingerprints quality enhancement, fingerprint feature extraction, security of fingerprint and performance improvement of AFRS. However, there are limited studies that address the application of DL to model fingerprint biometric for different tasks in the fingerprint recognition process. To bridge this gap, this paper presents a systematic literature review and an insightful meta-data analysis of a decade applications of DL in AFRS. Discussion on proposed model’s tasks, state of the art study, dataset, and training architecture are presented. The Convolutional Neural Networks models were the most saturated models in developing fingerprint biometrics authentication. The study revealed different roles of the DL in training architecture of the models: feature extractor, classifier and end-to-end learning. The review highlights open research challenges and present new perspective for solving the challenges in the future. The author believed that this paper will guide researchers in propose novel fingerprint authentication scheme.

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Section: Hybrid Deep Learning Approach For Fingerprintmentioning

confidence: 99%

Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Chiroma¹

2021

AIS

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“…This model adds total variation of the generated image to the GAN loss function, to promote the connectivity of generated fingerprint images. In [135], Tabassi et al developed a framework to synthesize altered fingerprints whose characteristics are similar to true altered fingerprints, and used them to train a classifier to detect "Fingerprint alteration/obfuscation presentation attack" (i.e. intentional tamper or damage to the real friction ridge patterns to avoid identification).…”

Section: Deep Learning Work On Fingerprint Recognitionmentioning

confidence: 99%

Biometrics Recognition Using Deep Learning: A Survey

Minaee¹,

Abdolrashidi²,

Su³

et al. 2019

Preprint

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Deep learning-based models have been very successful in achieving state-of-the-art results in many of the computer vision, speech recognition, and natural language processing tasks in the last few years. These models seem a natural fit for handling the everincreasing scale of biometric recognition problems, from cellphone authentication to airport security systems. Deep learning-based models have increasingly been leveraged to improve the accuracy of different biometric recognition systems in recent years. In this work, we provide a comprehensive survey of more than 120 promising works on biometric recognition (including face, fingerprint, iris, palmprint, ear, voice, signature, and gait recognition), which deploy deep learning models, and show their strengths and potentials in different applications. For each biometric, we first introduce the available datasets that are widely used in the literature and their characteristics. We will then talk about several promising deep learning works developed for that biometric, and show their performance on popular public benchmarks. We will also discuss some of the main challenges while using these models for biometric recognition, and possible future directions to which research in this area is headed.

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“…fabricated finger-like objects with an accurate imitation of one's fingerprint to steal their identity. Other forms of presentation attacks include use of altered fingerprints [11], [12], i.e. intentionally tampered or damaged real fingerprint patterns to avoid identification, and cadaver fingers [13].…”

Section: Introductionmentioning

confidence: 99%

Fingerprint Spoof Detector Generalization

Chugh

Jain

2021

IEEE Trans.Inform.Forensic Secur.

Self Cite

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We present a style-transfer based wrapper, called Universal Material Generator (UMG), to improve the generalization performance of any fingerprint spoof detector against spoofs made from materials not seen during training. Specifically, we transfer the style (texture) characteristics between fingerprint images of known materials with the goal of synthesizing fingerprint images corresponding to unknown materials, that may occupy the space between the known materials in the deep feature space. Synthetic live fingerprint images are also added to the training dataset to force the CNN to learn generative-noise invariant features which discriminate between lives and spoofs. The proposed approach is shown to improve the generalization performance of a state-of-the-art spoof detector, namely Fingerprint Spoof Buster, from TDR of 75.24% to 91.78% @ FDR = 0.2%. These results are based on a large-scale dataset of 5,743 live and 4,912 spoof images fabricated using 12 different materials. Additionally, the UMG wrapper is shown to improve the average cross-sensor spoof detection performance from 67.60% to 80.63% when tested on the LivDet 2017 dataset. Training the UMG wrapper requires only 100 live fingerprint images from the target sensor, alleviating the time and resources required to generate large-scale live and spoof datasets for a new sensor. We also fabricate physical spoof artifacts using a mixture of known spoof materials to explore the role of cross-material style transfer in improving generalization performance.

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Altered Fingerprints: Detection and Localization

Cited by 12 publications

References 12 publications

Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Biometrics Recognition Using Deep Learning: A Survey

Fingerprint Spoof Detector Generalization

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