Fingerprint Image Enhancement for Crime Detection Using Deep Learning

Anusha, Konakanchi; Kumar, Prashant

doi:10.1007/978-981-19-2358-6_25

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…In the third approach, the distortion is removed before the matching stage. Anusha and Siva Kumar [35] reported a CNN-based algorithm to detect and rectify latent distorted fingerprints. Despite achieving a high accuracy rate of 99% recognition rate, appropriate enhancement should be given to CNN first.…”

Section: Related Workmentioning

confidence: 99%

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A Coarse-to-Fine Approach for Rectifying Distorted Latent Fingerprints From Crime Scenes

Bao,

Wang,

Gao

et al. 2023

IEEE Access

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With the widely used fingerprints to identify criminals, the influence of fingerprint deformation arouses attention in forensic science. To date, many approaches have been proposed to rectify distorted fingerprints. However, the performance in handling low-quality fingerprints extracted at a crime scene is less high quality than expected. This paper presents a combined method to rectify the latent fingerprints extracted at a crime scene. The method is a coarse-to-fine approach, combining the robustness of traditional pattern recognition and the accuracy of deep learning networks. We conducted several experiments to compare our approach with other approaches, including the nearest-neighbor search and network methods. The results show a remarkable improvement in fingerprint matching, especially in lowquality latent fingerprints. The top 25 cumulative match rate improves from 0.65 (original) to 1 (proposed method), whereas other approaches improve the result to 0.85 at best.

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Section: Related Workmentioning

confidence: 99%

“…In the field of forensic science, meeting the criteria for the reference fingerprints cited in the literature (references [15], [16], [19], [20], [22], [35]) is challenging. Single latent distorted fingerprints are frequently encountered in CSI.…”

Section: ) Dcnn Partmentioning

confidence: 99%

A Coarse-to-Fine Approach for Rectifying Distorted Latent Fingerprints From Crime Scenes

Bao,

Wang,

Gao

et al. 2023

IEEE Access

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“…Image edge detection is a critical technology in the field of digital image processing. This technique is applied in various domains such as fingerprint detection [1][2][3], face recognition [4][5][6], image segmentation [7][8][9][10], and more. Hence, the precision of edge detection is a crucial aspect of applications that rely on edge detection.…”

Section: Introductionmentioning

confidence: 99%

Image Edge Detection Based on Fractional-Order Ant Colony Algorithm

Liu

2023

Fractal Fract

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Edge detection is a highly researched topic in the field of image processing, with numerous methods proposed by previous scholars. Among these, ant colony algorithms have emerged as a promising approach for detecting image edges. These algorithms have demonstrated high efficacy in accurately identifying edges within images. For this paper, due to the long-term memory, nonlocality, and weak singularity of fractional calculus, fractional-order ant colony algorithm combined with fractional differential mask and coefficient of variation (FACAFCV) for image edge detection is proposed. If we set the order of the fractional-order ant colony algorithm and fractional differential mask to ν = 0 , the edge detection method we propose becomes an integer-order edge detection method. We conduct experiments on images that are corrupted by multiplicative noise, as well as on an edge detection dataset. Our experimental results demonstrate that our method is able to detect image edges, while also mitigating the impact of multiplicative noise. These results indicate that our method has the potential to be a valuable tool for edge detection in practical applications.

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