Abstract
Cheiloscopy is a technique of forensic investigation with the purpose of identifying humans based on their lip prints. Analyzing the lip prints in detail, detailed characteristics could be deciphered, establishing a unique link with a specific person, thus helping in identification in persons using lip prints. Machine learning has significant applications in this forensic identification process with cheiloscopy, spanning from data collection to intelligent analysis. In this work, a design for a forensic decision support system has been proposed, aimed at identification of persons in terms of their biological sex based on cheiloscopy. In this respect, a generalized architecture for the implementation of cheiloscopy has been presented, along with the predictive modeling with lip prints using supervised algorithms, which has illustrated reasonable accuracy in identifying persons in terms of their biological sex.
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This work was supported and financed by the Cloudgenia group through its technical and operational capabilities for the design of the decision support system architecture.
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Sabelli, A., Chatterjee, P., Pollo-Cattaneo, M.F. (2022). Predictive Modeling Toward the Design of a Forensic Decision Support System Using Cheiloscopy for Identification from Lip Prints. In: Florez, H., Gomez, H. (eds) Applied Informatics. ICAI 2022. Communications in Computer and Information Science, vol 1643. Springer, Cham. https://doi.org/10.1007/978-3-031-19647-8_26
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DOI: https://doi.org/10.1007/978-3-031-19647-8_26
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