A Fingerprint plays an important role in identifying an individual in forensic and criminal investigations. Fingerprint ridge density is considered one of the most important features for sex classification. The present study intends to classify sex using fingerprint ridge density through a machine learning model, i.e., Random Forest. A total of 2040 fingerprints of 204 participants (102 males and 102 females) were collected from the north Indian population using a standard methodology. Ridge density in the three topological areas of fingerprints,i.e., radial, ulnar, and proximal areas, was assessed. Taking all the areas into consideration, the data of fingerprint ridge density was used to train the Random forest algorithm. The training and testing of the model data were taken in a ratio of 70:30, respectively (training dataset = 1428; testing dataset = 612). Random forest provided an accuracy of 81.53% in sex classification using fingerprint ridge density. The paper discusses the evaluation report of the accuracy of the parameters of the Random forest in detail. The study concludes that the machine learning models, such as Random forest can be utilized for sex classification from fingerprint ridge density. The study proposes its direct application in forensic examinations, especially when there is no clue about the perpetrator, and the sex of the perpetrator can be predicted from fingerprints recovered from the crime scene using the present customized model.
Fingermarks are frequently left on metal surfaces such as kitchen utensils, door handles, or elevator buttons in crime scenes. They are crucial forensic evidence to identify individuals and link them to crimes. Fingermark development on metal surfaces targets either the fingermark residues or the substrate. This study aimed to develop a rapid fingermark development method based on displacement reactions between copper (II) sulphate and various types of metal substrates, such as brass, galvanized iron, and low-carbon steel. Immersion of the metal substrate was more effective in fingermark visualization than applying the solution using a dropper. The optimized concentrations of copper (II) sulphate solution for fingermark visualization were found to be 0.7 M for brass, 0.5 M for galvanized iron, and 0.2 M for low-carbon steel. Sebaceous-rich fingermarks were visualized after the 5th depletion on brass and galvanized iron, and even after the 7th depletion on low-carbon steel. Further improvement is required before incorporating the application of copper (II) sulphate onto metal substrates to visualize fingermarks in real crime cases, due to the destructive nature of substrate submersion.
