Mgaga, Sboniso STapamo, JRBebis, G2021-02-182021-02-182020-12Mgaga, S.S. & Tapamo, J. 2020. Optical coherence tomography latent fingerprint image denoising. In <i>15th International Symposium on Visual Computing, San Diego, CA, USA, 5-7 October 2020. Published in: Bebis G. et al. (eds) Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science, vol 12510. Springer, Cham. https://doi.org/10.1007/978-3-030-64559-5_55</i>. G. Bebis, Ed. S.l.: Springer. http://hdl.handle.net/10204/11792 .978-3-030-64555-7http://hdl.handle.net/10204/11792Latent fingerprints are fingerprint impressions left on the surfaces a finger comes into contact with. They are found in almost every crime scene. Conventionally, latent fingerprints have been obtained using chemicals or physical methods, thus destructive techniques. Forensic community is moving towards contact-less acquisition methods. The contact-less acquisition presents some advantages over destructive methods; such advantages include multiple acquisitions of the sample and a possibility of further analysis such as touch DNA. This work proposes a speckle-noise denoising method for optical coherence tomography (OCT) latent fingerprint images. The proposed denoising technique was derived from the adaptive threshold and the normal shrinkage. Experimental results have shown that the proposed method suppressed specklenoise better than the adaptive threshold, NormalShrink, VisuShrink, SUREShrink and BayesShrink.AbstractenDenoisingLatent fingerprintsWavelet thresholdingOptical coherence tomographyBiometricsBook ChapterMgaga, S. S., & Tapamo, J. (2020). Optical coherence tomography latent fingerprint image denoising. In G. Bebis. (Ed.), <i>15th International Symposium on Visual Computing, San Diego, CA, USA, 5-7 October 2020. Published in: Bebis G. et al. (eds) Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science, vol 12510. Springer, Cham. https://doi.org/10.1007/978-3-030-64559-5_55</i> Springer. http://hdl.handle.net/10204/11792Mgaga, Sboniso S, and JR Tapamo. "Optical coherence tomography latent fingerprint image denoising" In <i>15TH INTERNATIONAL SYMPOSIUM ON VISUAL COMPUTING, SAN DIEGO, CA, USA, 5-7 OCTOBER 2020. PUBLISHED IN: BEBIS G. ET AL. (EDS) ADVANCES IN VISUAL COMPUTING. ISVC 2020. LECTURE NOTES IN COMPUTER SCIENCE, VOL 12510. SPRINGER, CHAM. HTTPS://DOI.ORG/10.1007/978-3-030-64559-5_55</i>, edited by G Bebis. n.p.: Springer. 2020. http://hdl.handle.net/10204/11792.Mgaga SS, Tapamo J. Optical coherence tomography latent fingerprint image denoising. In Bebis G, editor.. 15th International Symposium on Visual Computing, San Diego, CA, USA, 5-7 October 2020. Published in: Bebis G. et al. (eds) Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science, vol 12510. Springer, Cham. https://doi.org/10.1007/978-3-030-64559-5_55. [place unknown]: Springer; 2020. [cited yyyy month dd]. http://hdl.handle.net/10204/11792.TY - Book Chapter AU - Mgaga, Sboniso S AU - Tapamo, JR AB - Latent fingerprints are fingerprint impressions left on the surfaces a finger comes into contact with. They are found in almost every crime scene. Conventionally, latent fingerprints have been obtained using chemicals or physical methods, thus destructive techniques. Forensic community is moving towards contact-less acquisition methods. The contact-less acquisition presents some advantages over destructive methods; such advantages include multiple acquisitions of the sample and a possibility of further analysis such as touch DNA. This work proposes a speckle-noise denoising method for optical coherence tomography (OCT) latent fingerprint images. The proposed denoising technique was derived from the adaptive threshold and the normal shrinkage. Experimental results have shown that the proposed method suppressed specklenoise better than the adaptive threshold, NormalShrink, VisuShrink, SUREShrink and BayesShrink. DA - 2020-12 DB - ResearchSpace DP - CSIR ED - Bebis, G J1 - 15th International Symposium on Visual Computing, San Diego, CA, USA, 5-7 October 2020. Published in: Bebis G. et al. (eds) Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science, vol 12510. Springer, Cham. https://doi.org/10.1007/978-3-030-64559-5_55 KW - Denoising KW - Latent fingerprints KW - Wavelet thresholding KW - Optical coherence tomography KW - Biometrics LK - https://researchspace.csir.co.za PY - 2020 SM - 978-3-030-64555-7 T1 - Optical coherence tomography latent fingerprint image denoising TI - Optical coherence tomography latent fingerprint image denoising UR - http://hdl.handle.net/10204/11792 ER -24090