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Computer vision methods for cranial sex estimation

Arigbabu, Olasimbo Ayodeji ; Liao, Iman Yi ; Abdullah, Nurliza ; Mohamad Noor, Mohamad Helmee

IPSJ transactions on computer vision and applications, 2017-09, Vol.9 (1), p.1-15, Article 19 [Periódico revisado por pares]

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Título:
Computer vision methods for cranial sex estimation
Autor: Arigbabu, Olasimbo Ayodeji ; Liao, Iman Yi ; Abdullah, Nurliza ; Mohamad Noor, Mohamad Helmee
Assuntos: Algorithms ; Artificial Intelligence ; Computer Imaging ; Computer Science ; Computer vision ; Evaluation ; Image Processing and Computer Vision ; Pattern Recognition and Graphics ; Research Paper ; Robotics and Automation ; Sex ; User Interfaces and Human Computer Interaction ; Vision
É parte de: IPSJ transactions on computer vision and applications, 2017-09, Vol.9 (1), p.1-15, Article 19
Descrição: The objective of this study is to demonstrate through empirical evaluation the potential of a number of computer vision (CV) methods for sex determination from human skull. To achieve this, six local feature representations, two feature learnings, and three classification algorithms are rigorously combined and evaluated on skull regions derived from skull partitions. Furthermore, we introduce for the first time the application of multi-kernel learning (MKL) on multiple features for sex prediction from human skull. In comparison to the classical forensic methods, the results in this study are competitive, attesting to the suitability of CV methods for sex estimation. The proposed approach is fully automatic.
Editor: Berlin/Heidelberg: Springer Berlin Heidelberg
Idioma: Inglês

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