Face Verification with Veridical and Caricatured Images using Prominent Attributes

Caricatures, with their exaggerated features, offer a surprisingly efficient means for individuals to recognize each other compared to veridical (real) images. However, it is still a difficult task in machine learning to match veridical images to caricatures. This is due to the poor quality of caricature datasets, which often lack clear labels and contain low-quality images. Widely utilized veridical image datasets like CelebA also suffer from inadequate labeling. These label inconsistencies pose significant issues in accurate face verification tasks. Moreover, the effectiveness of neural networks has led to a shift in focus away from attribute-based representations, further complicating the matching process. In this thesis, we introduce a classification protocol for prominent facial feature recognition along with 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 feature labels.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Master's Degree

Grantor

University of Nevada, Reno

Year dc:date.issued

2024

Author dc:creator

• Sutariya, Jayam V.

Advisor dc:contributor.advisor

• Hand, Emily

Committee members dc:contributor.committeemember

• Akter Anima, Bashira
• Gumus, Mehmet

• Caricatures
• Explainability
• Face Verification
• Facial Attributes
• Feature Classification

Statement dc:rights

• Creative Commons Attribution-NonCommercial 4.0 International

Repository record dc:identifier.uri

https://scholarwolf.unr.edu/handle/11714/11030

OAI identifier oai:identifier

oai:scholarwolf.unr.edu:11714/11030