The objective of this study is to demonstrate through empirical evaluation the potential of a number of computer\nvision (CV) methods for sex determination from human skull. To achieve this, six local feature representations, two\nfeature learnings, and three classification algorithms are rigorously combined and evaluated on skull regions derived\nfrom skull partitions. Furthermore, we introduce for the first time the application of multi-kernel learning (MKL) on\nmultiple features for sex prediction from human skull. In comparison to the classical forensic methods, the results in\nthis study are competitive, attesting to the suitability of CV methods for sex estimation. The proposed approach is fully\nautomatic.
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