Pyramid histograms of oriented gradient for age and gender recognition using finger veins

Abstract

Nowadays, the COVID-19 pandemic imposes the use of a contactless biometric system to prevent the spread of contagious diseases efficiently. Hand veins are contactless and independent of the body’s appearance. However, researches on age and gender estimation by hand veins are very limited. They focused only on age group discrimination and not the exact age. Estimating the age and gender by veins features is a challenging task since hand vein images are poor in quality and subject to variation in illumination. In this paper, a finger vein gender and age recognition system based on Pyramidal histograms of oriented gradient (PHOG) is presented. PHOG can better describe both the local shape and the spatial distribution of the veins as the image is divided into sub-regions at different resolutions for which the HOG descriptor is applied. Experimental validation on finger vein databases MMCBNU 6000 and UTFVP demonstrates the effectiveness of extracted features in gender classification and age estimation including ages from 16 to 72 years with an uncertainty of one year. The middle finger of the left hand provides the best results for both age and gender classification (F-measure 100%) for MMCBNU 6000 database, whereas for UTFVP database, F-measure is about 98,62% for age estimation and 99,47% for gender classification. A comparison study with recent approaches is carried on, showing an improvement of F-measure by 5.76% for age estimation and 1.38% for gender classification.