Lip Print Patterns in Qassim: Demographic Variations Using Suzuki and Tsuchihashi Classification

Objectives: Forensic scientists now recognize lip print research known as cheiloscopy as a valuable method for identifying people which supplements fingerprints and DNA examination. Although lip prints are unique and permanent features they receive limited use in forensic analysis of certain population groups. Aim: The study analyzes how gender and age influence lip prints among residents of Qassim Saudi Arabia according to Suzuki and Tsuchihashi’s classification system. Methods: The study's design involved 100 participants selected by age from 18 to 45 among 50 males and equal female participants. Eye examination revealed lip print classifications that researchers collected using non-invasive method involving a lipstick and transparent film. Investigators used the Chi-square test to evaluate whether demographic variables showed an association with lip print distribution types. Results: The majority of participants showed Type I lip print patterns which accounted for 44% of all participants. The results showed that female participants displayed Type I patterns in 70% of cases but male participants mostly had Type IV patterns at 42%. A measurement analysis showed that lips print distribution across genders differed significantly in lip print frequencies (χ² = 15.72, p < 0.01). Lip print type demonstrated no statistical connection with the participants' age divisions (p > 0.05). Limitations: The research contained a restricted participant number alongside a bias toward younger individuals which potentially reduced the study's ability to generalize its findings. The assessment process by manual methods along with missing power analysis measurements warrant attention because they could lead to classification bias. Conclusion: Interpretation of lip prints showed important distinctions based on gender thus strengthening cheiloscopy as an investigative aid during forensic investigations. Additional research of large size using artificial intelligence should be supported to study multiple population groups