CarVer: Setting the Standard for Face Verification with Caricatures

Research in psychology and neuroscience point to the potential of caricatures to improve human face recognition in non-ideal settings. Face verification using caricatures is the first step in using caricatures in this way, which involves verifying pairs of faces where one is veridical (realistic) and the other is a caricature. Existing biometric research in the area of caricature face verification is recent and limited by a lack of well-constructed, large-scale datasets. We show that the largest available dataset, WebCaricature, falls short of acceptable standards of image quality and representation necessary for face verification. We present a new caricature dataset, CarVer, and introduce a face verification evaluation standard to replace WebCaricature’s. We find that the CarVer dataset (F1=0.82) outperforms WebCaricature (F1=0.76) using WebCaricature’s Bounding Box alignment method. Additionally, our new alignment methods, Enlarged Bounding Box (F1=0.82) and Revised Bounding Box (F1=0.80) outperform WebCaricature’s Bounding Box (F1=0.72) and Eye Location (F1=0.74) alignment methods.