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Latent Fingerprint Enhancement Based on EDTV Model

Latent Fingerprint Enhancement Based on EDTV Model

Shadi M. S. Hilles, Abdilahi Deria Liban, Abdullah M. M. Altrad, Yousef A. Baker El-Ebiary, Mohanad M. Hilles
Copyright: © 2021 |Pages: 16
ISBN13: 9781799869856|ISBN10: 1799869857|EISBN13: 9781799869863
DOI: 10.4018/978-1-7998-6985-6.ch021
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MLA

Hilles, Shadi M. S., et al. "Latent Fingerprint Enhancement Based on EDTV Model." Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry, edited by Valentina Chkoniya, IGI Global, 2021, pp. 431-446. https://doi.org/10.4018/978-1-7998-6985-6.ch021

APA

Hilles, S. M., Liban, A. D., Altrad, A. M., El-Ebiary, Y. A., & Hilles, M. M. (2021). Latent Fingerprint Enhancement Based on EDTV Model. In V. Chkoniya (Ed.), Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry (pp. 431-446). IGI Global. https://doi.org/10.4018/978-1-7998-6985-6.ch021

Chicago

Hilles, Shadi M. S., et al. "Latent Fingerprint Enhancement Based on EDTV Model." In Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry, edited by Valentina Chkoniya, 431-446. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-6985-6.ch021

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Abstract

The chapter presents latent fingerprint enhancement technique for enforcement agencies to identify criminals. There are many challenges in the area of latent fingerprinting due to poor-quality images, which consist of unclear ridge structure and overlapping patterns with structure noise. Image enhancement is important to suppress several different noises for improving accuracy of ridge structure. The chapter presents a combination of edge directional total variation model, EDTV, and quality image enhancement with lost minutia re-construction, RMSE, for evaluation and performance in the proposed algorithm. The result shows the average of three different image categories which are extracted from the SD7 dataset, and the assessments are good, bad, and ugly, respectively. The result of RMSE before and after enhancement shows the performance ratio of the proposed method is better for latent fingerprint images compared to bad and ugly images while there is not much difference with performance of bad and ugly.

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