Face Verification with Veridical and Caricatured Images using Prominent Attributes

Caricatures, with their exaggerated features, offer an efficient means for individuals to recognize each other compared to veridical (real) images. However, matching veridical images to caricatures remains a challenging task in machine learning. This difficulty stems from poor quality caricature datasets lacking clear labels, inadequate labeling in widely used veridical image datasets like CelebA, and a shift away from attribute-based representations due to the rise of neural networks. These issues significantly impact the accuracy of face verification tasks between caricatures and veridical images. To address these challenges, this paper introduces a classification protocol for prominent facial feature recognition and a verification protocol for matching celebrity veridical images to their caricatures. We utilize CarVer, a recently curated dataset comprising both veridical and caricature images accompanied by detailed prominent attribute labels. Our approach aims to develop a set of prominent facial attributes that can effectively represent both real and caricatured images, enabling improved face verification across these modalities. This research has potential applications across various industries where robust cross-modal face recognition is crucial.