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Weber A, Hoplight B, Ogilvie R, Muro C, Khandasammy SR, Pérez-Almodóvar L, Sears S, Lednev IK. Innovative Vibrational Spectroscopy Research for Forensic Application. Anal Chem 2023; 95:167-205. [PMID: 36625116 DOI: 10.1021/acs.analchem.2c05094] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Alexis Weber
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States.,SupreMEtric LLC, 7 University Pl. B210, Rensselaer, New York 12144, United States
| | - Bailey Hoplight
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Rhilynn Ogilvie
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Claire Muro
- New York State Police Forensic Investigation Center, Building #30, Campus Access Rd., Albany, New York 12203, United States
| | - Shelby R Khandasammy
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Luis Pérez-Almodóvar
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Samuel Sears
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Igor K Lednev
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States.,SupreMEtric LLC, 7 University Pl. B210, Rensselaer, New York 12144, United States
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Bécue A, Champod C. Interpol review of fingermarks and other body impressions 2019 - 2022). Forensic Sci Int Synerg 2022; 6:100304. [PMID: 36636235 PMCID: PMC9830181 DOI: 10.1016/j.fsisyn.2022.100304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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Automatic Fingerprint Classification Using Deep Learning Technology (DeepFKTNet). MATHEMATICS 2022. [DOI: 10.3390/math10081285] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Fingerprints are gaining in popularity, and fingerprint datasets are becoming increasingly large. They are often captured utilizing a variety of sensors embedded in smart devices such as mobile phones and personal computers. One of the primary issues with fingerprint recognition systems is their high processing complexity, which is exacerbated when they are gathered using several sensors. One way to address this issue is to categorize fingerprints in a database to condense the search space. Deep learning is effective in designing robust fingerprint classification methods. However, designing the architecture of a CNN model is a laborious and time-consuming task. We proposed a technique for automatically determining the architecture of a CNN model adaptive to fingerprint classification; it automatically determines the number of filters and the layers using Fukunaga–Koontz transform and the ratio of the between-class scatter to within-class scatter. It helps to design lightweight CNN models, which are efficient and speed up the fingerprint recognition process. The method was evaluated two public-domain benchmark datasets FingerPass and FVC2004 benchmark datasets, which contain noisy, low-quality fingerprints obtained using live scan devices and cross-sensor fingerprints. The designed models outperform the well-known pre-trained models and the state-of-the-art fingerprint classification techniques.
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