Identification of sex from footprint dimensions using machine learning: a study on population of Punjab in Pakistan

Egyptian Journal of Forensic Sciences

Muhammad Awais¹  Nouman Rasool²  Faizana Naeem³  Sajid Mahmood⁴ 

[1] Department of Information System, School of Business and Economics, University of Management and Technology, Lahore, Pakistan;Department of Life Sciences, School of Science, University of Management and Technology, Lahore, Pakistan;Department of Software Engineering, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan;Dr Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan

关键词: Footprints;    Sex classification;    Pakistani population;    Naïve Bayes;    J48;   

DOI  :  10.1186/s41935-018-0106-2

学科分类：生理学与病理学

来源: Springer

【 摘 要 】

Likewise the fingerprints and palm prints, footprints are also helpful in solving a crime puzzle; however, very few studies have been reported targeting the identification of sex-based upon footprint features. Therefore, the present study aims at the identification of sex using footprint features from the population of Punjab, Pakistan. The foot measurements, i.e., toe length ratio, individual toe lengths, foot breadth, and foot index, are used as features for the identification of sex. Footprint samples were collected from 280 volunteers (142 males and 138 females) from all over Punjab (age range 18–50 years). A sex identification method is proposed in this study employing various machine learning algorithms, i.e., Naïve Bayes, J48, Random Forest, Random Tree, and REP Tree, and compared them. The designed model was cross-validated using 10-fold cross-validation. The results demonstrated the varying accuracy of the machine learning algorithms, using different combinations of footprint features. However, the Naïve Bayes algorithm demonstrated an accuracy of 87.8%, for sex identification, using the combination of toe length and foot indexes. It is concluded that by using a combination of toe length and foot indexes and employing the Naïve Bayes algorithm, sex can be identified more accurately as compared to the other methods.

【 授权许可】

CC BY