Evaluation of Texture Descriptors for Automated Gender Estimation from Fingerprints

Gender is an important demographic attribute. In the context of biometrics, gender information can be used to index databases or enhance the recognition accuracy of primary biometric traits. A number of studies have demonstrated that gender can be automatically deduced from face images. However, few studies have explored the possibility of automatically estimating gender information from fingerprint images. Consequently, there is a limited understanding in this topic. Fingerprint being a widely adopted biometrics, gender cues from the fingerprint image will significantly aid in commercial applications and forensic investigations. This study explores the use of classical texture descriptors Local Binary Pattern (LBP), Local Phase Quantization (LPQ), Binarized Statistical Image Features (BSIF) and Local Ternary Pattern (LTP) to estimate gender from fingerprint images. The robustness of these descriptors to various types of image degradations is evaluated. Experiments conducted on the WVU fingerprint dataset suggest the efficacy of LBP descriptor in encoding gender information from good quality fingerprints. The BSIF descriptor is observed to be robust to noisy and partial fingerprints, while LPQ is observed to work well on blurred fingerprints. However, the gender estimation accuracy in the case of fingerprints is much lower than that of face, thereby suggesting that more work is necessary on this topic.