The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Anil K. Jain - One of the best experts on this subject based on the ideXlab platform.
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altered Fingerprints detection and localization
International Conference on Biometrics: Theory Applications and Systems, 2018Co-Authors: Elham Tabassi, Tarang Chugh, Debayan Deb, Anil K. JainAbstract:Fingerprint alteration, also referred to as obfuscation presentation attack, is to intentionally tamper or damage the real friction ridge patterns to avoid identification by an AFIS. This paper proposes a method for detection and localization of fingerprint alterations. Our main contributions are: (i) design and train CNN models on fingerprint images and minutiae-centered local patches in the image to detect and localize regions of fingerprint alterations, and (ii) train a Generative Adversarial Network (GAN) to synthesize altered Fingerprints whose characteristics are similar to true altered Fingerprints. A successfully trained GAN can alleviate the limited availability of altered fingerprint images for research. A database of 4,815 altered Fingerprints from 270 subjects, and an equal number of rolled fingerprint images are used to train and test our models. The proposed approach achieves a True Detection Rate (TDR) of 99.24% at a False Detection Rate (FDR) of 2%, outperforming published results. The altered fingerprint detection and localization model and code, and the synthetically generated altered fingerprint dataset will be open-sourced.
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altered Fingerprints detection and localization
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Elham Tabassi, Tarang Chugh, Debayan Deb, Anil K. JainAbstract:Fingerprint alteration, also referred to as obfuscation presentation attack, is to intentionally tamper or damage the real friction ridge patterns to avoid identification by an AFIS. This paper proposes a method for detection and localization of fingerprint alterations. Our main contributions are: (i) design and train CNN models on fingerprint images and minutiae-centered local patches in the image to detect and localize regions of fingerprint alterations, and (ii) train a Generative Adversarial Network (GAN) to synthesize altered Fingerprints whose characteristics are similar to true altered Fingerprints. A successfully trained GAN can alleviate the limited availability of altered fingerprint images for research. A database of 4,815 altered Fingerprints from 270 subjects, and an equal number of rolled fingerprint images are used to train and test our models. The proposed approach achieves a True Detection Rate (TDR) of 99.24% at a False Detection Rate (FDR) of 2%, outperforming published results. The synthetically generated altered fingerprint dataset will be open-sourced.
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fingerprint recognition of young children
IEEE Transactions on Information Forensics and Security, 2017Co-Authors: Anil K. Jain, Sunpreet S Arora, Kai Cao, Lacey Bestrowden, Anjoo BhatnagarAbstract:In 1899, Galton first captured ink-on-paper Fingerprints of a single child from birth until the age of 4.5 years, manually compared the prints, and concluded that “the print of a child at the age of 2.5 years would serve to identify him ever after.” Since then, ink-on-paper fingerprinting and manual comparison methods have been superseded by digital capture and automatic fingerprint comparison techniques, but only a few feasibility studies on child fingerprint recognition have been conducted. Here, we present the first systematic and rigorous longitudinal study that addresses the following questions: 1) Do Fingerprints of young children possess the salient features required to uniquely recognize a child? 2) If so, at what age can a child’s Fingerprints be captured with sufficient fidelity for recognition? 3) Can a child’s Fingerprints be used to reliably recognize the child as he ages? For this paper, we collected Fingerprints of 309 children (0–5 years old) four different times over a one year period. We show, for the first time, that Fingerprints acquired from a child as young as 6-h old exhibit distinguishing features necessary for recognition, and that state-of-the-art fingerprint technology achieves high recognition accuracy (98.9% true accept rate at 0.1% false accept rate) for children older than six months. In addition, we use mixed-effects statistical models to study the persistence of child fingerprint recognition accuracy and show that the recognition accuracy is not significantly affected over the one year time lapse in our data. Given rapidly growing requirements to recognize children for vaccination tracking, delivery of supplementary food, and national identification documents, this paper demonstrates that fingerprint recognition of young children (six months and older) is a viable solution based on available capture and recognition technology.
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longitudinal study of fingerprint recognition
Proceedings of the National Academy of Sciences of the United States of America, 2015Co-Authors: Soweon Yoon, Anil K. JainAbstract:Human identification by Fingerprints is based on the fundamental premise that ridge patterns from distinct fingers are different (uniqueness) and a fingerprint pattern does not change over time (persistence). Although the uniqueness of Fingerprints has been investigated by developing statistical models to estimate the probability of error in comparing two random samples of Fingerprints, the persistence of Fingerprints has remained a general belief based on only a few case studies. In this study, fingerprint match (similarity) scores are analyzed by multilevel statistical models with covariates such as time interval between two Fingerprints in comparison, subject's age, and fingerprint image quality. Longitudinal fingerprint records of 15,597 subjects are sampled from an operational fingerprint database such that each individual has at least five 10-print records over a minimum time span of 5 y. In regard to the persistence of Fingerprints, the longitudinal analysis on a single (right index) finger demonstrates that (i) genuine match scores tend to significantly decrease when time interval between two Fingerprints in comparison increases, whereas the change in impostor match scores is negligible; and (ii) fingerprint recognition accuracy at operational settings, nevertheless, tends to be stable as the time interval increases up to 12 y, the maximum time span in the dataset. However, the uncertainty of temporal stability of fingerprint recognition accuracy becomes substantially large if either of the two Fingerprints being compared is of poor quality. The conclusions drawn from 10-finger fusion analysis coincide with the conclusions from single-finger analysis.
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is there a fingerprint pattern in the image
International Conference on Biometrics, 2013Co-Authors: Soweon Yoon, Anil K. JainAbstract:A fingerprint orientation field has distinct characteristics which can differentiate Fingerprints from any other flow patterns: it has a specific number of singular points (cores and deltas), the configuration of singular points follows a certain spatial distribution, and its global shape is like an arch. In this paper, we propose a global fingerprint orientation field model, represented in terms of ordinary differential equations, which does not require any prior information such as singular points or orientation of a fingerprint. Further, the model requires only a small number of polynomial terms to represent the global fingerprint orientation field. The coefficients of the model are found subject to the constraints on the total number of singular points (i.e., 0, 2, or 4) in a fingerprint. The proposed model is used to distinguish fingerprint images from non-fingerprint images and altered Fingerprints by measuring the abnormality in the orientation field of the image.
Hao Hu - One of the best experts on this subject based on the ideXlab platform.
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quality traceability system of traditional chinese medicine based on two dimensional barcode using mobile intelligent technology
PLOS ONE, 2016Co-Authors: Xiwen Li, Runmiao Wang, Qing Yang, Peng Li, Hao HuAbstract:Currently, the chemical fingerprint comparison and analysis is mainly based on professional equipment and software, it’s expensive and inconvenient. This study aims to integrate QR (Quick Response) code with quality data and mobile intelligent technology to develop a convenient query terminal for tracing quality in the whole industrial chain of TCM (traditional Chinese medicine). Three herbal medicines were randomly selected and their chemical two-dimensional barcode (2D) barcodes Fingerprints were constructed. Smartphone application (APP) based on Android system was developed to read initial data of 2D chemical barcodes, and compared multiple Fingerprints from different batches of same species or different species. It was demonstrated that there were no significant differences between original and scanned TCM chemical Fingerprints. Meanwhile, different TCM chemical fingerprint QR codes could be rendered in the same coordinate and showed the differences very intuitively. To be able to distinguish the variations of chemical fingerprint more directly, linear interpolation angle cosine similarity algorithm (LIACSA) was proposed to get similarity ratio. This study showed that QR codes can be used as an effective information carrier to transfer quality data. Smartphone application can rapidly read quality information in QR codes and convert data into TCM chemical Fingerprints.
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traceability and quality control in traditional chinese medicine from chemical fingerprint to two dimensional barcode
Evidence-based Complementary and Alternative Medicine, 2015Co-Authors: Xiwen Li, Jingyun Ni, Xiaojia Chen, Hao Hu, Mei Li, Yitao WangAbstract:Chemical fingerprinting is currently a widely used tool that enables rapid and accurate quality evaluation of Traditional Chinese Medicine (TCM). However, chemical Fingerprints are not amenable to information storage, recognition, and retrieval, which limit their use in Chinese medicine traceability. In this study, samples of three kinds of Chinese medicines were randomly selected and chemical Fingerprints were then constructed by using high performance liquid chromatography. Based on chemical data, the process of converting the TCM chemical fingerprint into two-dimensional code is presented; preprocess and filtering algorithm are also proposed aiming at standardizing the large amount of original raw data. In order to know which type of two-dimensional code (2D) is suitable for storing data of chemical Fingerprints, current popular types of 2D codes are analyzed and compared. Results show that QR Code is suitable for recording the TCM chemical fingerprint. The fingerprint information of TCM can be converted into data format that can be stored as 2D code for traceability and quality control.
Xiwen Li - One of the best experts on this subject based on the ideXlab platform.
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quality traceability system of traditional chinese medicine based on two dimensional barcode using mobile intelligent technology
PLOS ONE, 2016Co-Authors: Xiwen Li, Runmiao Wang, Qing Yang, Peng Li, Hao HuAbstract:Currently, the chemical fingerprint comparison and analysis is mainly based on professional equipment and software, it’s expensive and inconvenient. This study aims to integrate QR (Quick Response) code with quality data and mobile intelligent technology to develop a convenient query terminal for tracing quality in the whole industrial chain of TCM (traditional Chinese medicine). Three herbal medicines were randomly selected and their chemical two-dimensional barcode (2D) barcodes Fingerprints were constructed. Smartphone application (APP) based on Android system was developed to read initial data of 2D chemical barcodes, and compared multiple Fingerprints from different batches of same species or different species. It was demonstrated that there were no significant differences between original and scanned TCM chemical Fingerprints. Meanwhile, different TCM chemical fingerprint QR codes could be rendered in the same coordinate and showed the differences very intuitively. To be able to distinguish the variations of chemical fingerprint more directly, linear interpolation angle cosine similarity algorithm (LIACSA) was proposed to get similarity ratio. This study showed that QR codes can be used as an effective information carrier to transfer quality data. Smartphone application can rapidly read quality information in QR codes and convert data into TCM chemical Fingerprints.
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traceability and quality control in traditional chinese medicine from chemical fingerprint to two dimensional barcode
Evidence-based Complementary and Alternative Medicine, 2015Co-Authors: Xiwen Li, Jingyun Ni, Xiaojia Chen, Hao Hu, Mei Li, Yitao WangAbstract:Chemical fingerprinting is currently a widely used tool that enables rapid and accurate quality evaluation of Traditional Chinese Medicine (TCM). However, chemical Fingerprints are not amenable to information storage, recognition, and retrieval, which limit their use in Chinese medicine traceability. In this study, samples of three kinds of Chinese medicines were randomly selected and chemical Fingerprints were then constructed by using high performance liquid chromatography. Based on chemical data, the process of converting the TCM chemical fingerprint into two-dimensional code is presented; preprocess and filtering algorithm are also proposed aiming at standardizing the large amount of original raw data. In order to know which type of two-dimensional code (2D) is suitable for storing data of chemical Fingerprints, current popular types of 2D codes are analyzed and compared. Results show that QR Code is suitable for recording the TCM chemical fingerprint. The fingerprint information of TCM can be converted into data format that can be stored as 2D code for traceability and quality control.
Stefan Goedecker - One of the best experts on this subject based on the ideXlab platform.
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maximum volume simplex method for automatic selection and classification of atomic environments and environment descriptor compression
Journal of Chemical Physics, 2020Co-Authors: Behnam Parsaeifard, Daniele Tomerini, Stefan GoedeckerAbstract:Fingerprint distances, which measure the similarity of atomic environments, are commonly calculated from atomic environment fingerprint vectors. In this work, we present the simplex method that can perform the inverse operation, i.e., calculating fingerprint vectors from fingerprint distances. The fingerprint vectors found in this way point to the corners of a simplex. For a large dataset of Fingerprints, we can find a particular largest simplex, whose dimension gives the effective dimension of the fingerprint vector space. We show that the corners of this simplex correspond to landmark environments that can be used in a fully automatic way to analyze structures. In this way, we can, for instance, detect atoms in grain boundaries or on edges of carbon flakes without any human input about the expected environment. By projecting Fingerprints on the largest simplex, we can also obtain fingerprint vectors that are considerably shorter than the original ones but whose information content is not significantly reduced.
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maximum volume simplex method for automatic selection and classification of atomic environments and environment descriptor compression
arXiv: Computational Physics, 2020Co-Authors: Behnam Parsaeifard, Daniele Tomerini, Stefan GoedeckerAbstract:Fingerprint distances, which measure the similarity of atomic environments, are commonly calculated from atomic environment fingerprint vectors. In this work we present the simplex method which can perform the inverse operation, i.e. calculating fingerprint vectors from fingerprint distances. The fingerprint vectors found in this way point to the corners of a simplex. For a large data set of Fingerprints, we can find a particular largest volume simplex, whose dimension gives the effective dimension of the fingerprint vector space. We show that the corners of this simplex correspond to landmark environments that can by used in a fully automatic way to analyse structures. In this way we can for instance detect atoms in grain boundaries or on edges of carbon flakes without any human input about the expected environment. By projecting Fingerprints on the largest volume simplex we can also obtain fingerprint vectors that are considerably shorter than the original ones but whose information content is not significantly reduced.
Yitao Wang - One of the best experts on this subject based on the ideXlab platform.
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traceability and quality control in traditional chinese medicine from chemical fingerprint to two dimensional barcode
Evidence-based Complementary and Alternative Medicine, 2015Co-Authors: Xiwen Li, Jingyun Ni, Xiaojia Chen, Hao Hu, Mei Li, Yitao WangAbstract:Chemical fingerprinting is currently a widely used tool that enables rapid and accurate quality evaluation of Traditional Chinese Medicine (TCM). However, chemical Fingerprints are not amenable to information storage, recognition, and retrieval, which limit their use in Chinese medicine traceability. In this study, samples of three kinds of Chinese medicines were randomly selected and chemical Fingerprints were then constructed by using high performance liquid chromatography. Based on chemical data, the process of converting the TCM chemical fingerprint into two-dimensional code is presented; preprocess and filtering algorithm are also proposed aiming at standardizing the large amount of original raw data. In order to know which type of two-dimensional code (2D) is suitable for storing data of chemical Fingerprints, current popular types of 2D codes are analyzed and compared. Results show that QR Code is suitable for recording the TCM chemical fingerprint. The fingerprint information of TCM can be converted into data format that can be stored as 2D code for traceability and quality control.
