A Neural Network-Based Partial Fingerprint Image Identification Method for Crime Scenes
Fingerprints are the most widely used of all biological characteristics in public safety and forensic identification. However, fingerprint images extracted from the crime scene are incomplete. On the one hand, due to the lack of effective area in partial fingerprint images, the extracted features ar...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/2/1188 |
| _version_ | 1797446442390913024 |
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| author | Yuting Sun Yanfeng Tang Xiaojuan Chen |
| author_facet | Yuting Sun Yanfeng Tang Xiaojuan Chen |
| author_sort | Yuting Sun |
| collection | DOAJ |
| description | Fingerprints are the most widely used of all biological characteristics in public safety and forensic identification. However, fingerprint images extracted from the crime scene are incomplete. On the one hand, due to the lack of effective area in partial fingerprint images, the extracted features are insufficient. On the other hand, a broken ridge may lead to a large number of false feature points, which affect the accuracy of fingerprint recognition. Existing fingerprint identification methods are not ideal for partial fingerprint identification. To overcome these problems, this paper proposes an attention-based partial fingerprint identification model named APFI. Firstly, the algorithm utilizes the residual network (ResNet) for feature descriptor extraction, which generates a representation of spatial information on fingerprint expression. Secondly, the channel attention module is inserted into the proposed model to obtain more accurate fingerprint feature information from the residual block. Then, to improve the identification accuracy of partial fingerprints, the angular distance between features is used to calculate the similarity of fingerprints. Finally, the proposed model is trained and validated on a home-made partial fingerprint image dataset. Experiments on the home-made fingerprint datasets and the NIST-SD4 datasets show that the partial fingerprint identification method proposed in this paper has higher identification accuracy than other state-of-the-art methods. |
| first_indexed | 2024-03-09T13:40:35Z |
| format | Article |
| id | doaj.art-a09234281470413b8ca407c27d137caf |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T13:40:35Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a09234281470413b8ca407c27d137caf2023-11-30T21:07:26ZengMDPI AGApplied Sciences2076-34172023-01-01132118810.3390/app13021188A Neural Network-Based Partial Fingerprint Image Identification Method for Crime ScenesYuting Sun0Yanfeng Tang1Xiaojuan Chen2Department of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaDepartment of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaDepartment of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaFingerprints are the most widely used of all biological characteristics in public safety and forensic identification. However, fingerprint images extracted from the crime scene are incomplete. On the one hand, due to the lack of effective area in partial fingerprint images, the extracted features are insufficient. On the other hand, a broken ridge may lead to a large number of false feature points, which affect the accuracy of fingerprint recognition. Existing fingerprint identification methods are not ideal for partial fingerprint identification. To overcome these problems, this paper proposes an attention-based partial fingerprint identification model named APFI. Firstly, the algorithm utilizes the residual network (ResNet) for feature descriptor extraction, which generates a representation of spatial information on fingerprint expression. Secondly, the channel attention module is inserted into the proposed model to obtain more accurate fingerprint feature information from the residual block. Then, to improve the identification accuracy of partial fingerprints, the angular distance between features is used to calculate the similarity of fingerprints. Finally, the proposed model is trained and validated on a home-made partial fingerprint image dataset. Experiments on the home-made fingerprint datasets and the NIST-SD4 datasets show that the partial fingerprint identification method proposed in this paper has higher identification accuracy than other state-of-the-art methods.https://www.mdpi.com/2076-3417/13/2/1188partial fingerprint identificationbiometric identificationdeep learningfeatures extraction |
| spellingShingle | Yuting Sun Yanfeng Tang Xiaojuan Chen A Neural Network-Based Partial Fingerprint Image Identification Method for Crime Scenes Applied Sciences partial fingerprint identification biometric identification deep learning features extraction |
| title | A Neural Network-Based Partial Fingerprint Image Identification Method for Crime Scenes |
| title_full | A Neural Network-Based Partial Fingerprint Image Identification Method for Crime Scenes |
| title_fullStr | A Neural Network-Based Partial Fingerprint Image Identification Method for Crime Scenes |
| title_full_unstemmed | A Neural Network-Based Partial Fingerprint Image Identification Method for Crime Scenes |
| title_short | A Neural Network-Based Partial Fingerprint Image Identification Method for Crime Scenes |
| title_sort | neural network based partial fingerprint image identification method for crime scenes |
| topic | partial fingerprint identification biometric identification deep learning features extraction |
| url | https://www.mdpi.com/2076-3417/13/2/1188 |
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