Abstract
BackgroundLikewise 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.ResultsThe 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.ConclusionsIt 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.
Highlights
Likewise the fingerprints and palm prints, footprints are helpful in solving a crime puzzle; very few studies have been reported targeting the identification of sex-based upon footprint features
The results demonstrated that all footprint dimensions have shown significant results except Heel breadth (HB) index
Comparison between the mean of right and left foot In males, a greater mean value was observed for foot toe 1 and ball breadth index of the left foot, while a higher mean value was observed for the heel breadth index of the right foot in comparison to the left foot
Summary
Likewise the fingerprints and palm prints, footprints are helpful in solving a crime puzzle; very few studies have been reported targeting the identification of sex-based upon footprint features. The present study aims at the identification of sex using footprint features from the population of Punjab, Pakistan. Fingerprints and palm prints are used by forensic officials for identification (Uthman et al, 2012). Like fingerprints and palm prints, footprints are widely recovered as pieces of evidence from the crime scenes. It is evident from the reported cases that culprits mostly remove their footwear to reduce the noise of walking, leaving their foot impressions at the crime scene (Khan and Moorthy, 2013).
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