Age and Gender Classification from Finger Vein Patterns.

The main goal of this paper is to build a system able to recognize the age range and the gender of individuals from their venous network characteristic. Accordingly, we develop an algorithm able to detect changes related to aging. Proposed age and gender recognition system is composed by 4 key steps: image acquisition, image preprocessing, feature extraction and age/gender classification. Image preprocessing is established by ROI extraction and image enhancement. ROI extraction separates the informative region from finger vein image. For image enhancement, we use Guided Filter based Singe Scale Retinex (GFSSR) method. In feature extraction step, we implement the LBP descriptor in order to characterize venous texture from finger veins. Our study is based on MMCBNU 6000 finger vein database. Experimental results prove that extracted attributes from finger vein can define the gender and the age class. Proposed age and gender classification process gives a recognition rate of 98% for gender classification and a recognition rate of 99.67%, 99.78% and 97.33% for respectively 2, 3 and 4 classes, for age classification.