Fusion of Local Binary Pattern and Local Phase Quantization features set for Gender Classification using Fingerprints - Kruthi, R; Patil, Abhijit; Gornale, Shivanand (International Journal of Computer Sciences and Engineering, 2019)
Gender identification of an individual is a fundamental task, as many social interactions are gender-based. The fingerprint is the most precise and reliable biometric trait for gender identification. It plays a vital role to link the suspect in a crime scene or to find an unknown person. The gender identification can significantly enhance the performance of authentication systems and reduces the search space and speed up the matching rate. Several previous studies have investigated the gender identification from fingerprints but lack’s in conventional results. In this work, the authors propose gender identification based on fingerprints by using the fusion of two well-known local descriptors, such as LBP and LPQ. The proposed algorithm is evaluated on state of two datasets i.e. publically available SDUMLA-HMT fingerprint dataset and other is self-created fingerprint dataset, which embraces fingerprints of 348 individuals (10 samples from each individual) of which 183 are males and 165 are female volunteers and obtained the best classification rate of 97% accuracy using SVM classifier. The results are competitive and appreciable as compared to earlier methods.