How Unique Is a Face: An Investigative Study
This study addresses the lack of understanding regarding the distinctiveness of faces for the face recognition community, providing early quantitative insights into factors affecting biometric uniqueness.
This paper investigates the uniqueness of faces as a biometric modality by studying the impact of factors like image resolution, feature representation, database size, age, and gender on the Kullback-Leibler divergence between genuine and impostor distributions. The study uses datasets such as AT&T, LFW, IMDb-Face, and ND-TWINS, with various feature extraction algorithms including VGGFace and ResNet50, to quantitatively assess these impacts.
Face recognition has been widely accepted as a means of identification in applications ranging from border control to security in the banking sector. Surprisingly, while widely accepted, we still lack the understanding of uniqueness or distinctiveness of faces as biometric modality. In this work, we study the impact of factors such as image resolution, feature representation, database size, age and gender on uniqueness denoted by the Kullback-Leibler divergence between genuine and impostor distributions. Towards understanding the impact, we present experimental results on the datasets AT&T, LFW, IMDb-Face, as well as ND-TWINS, with the feature extraction algorithms VGGFace, VGG16, ResNet50, InceptionV3, MobileNet and DenseNet121, that reveal the quantitative impact of the named factors. While these are early results, our findings indicate the need for a better understanding of the concept of biometric uniqueness and its implication on face recognition.