Identity Level Attributes for Explainable Facial Verification

Attributes are describable features of faces and are the foundational visual component that humans use to recognize faces. Current state-of-the-art facial recognition methods are end-to-end deep learning systems that are not interpretable by humans. In this thesis, we introduce a new type of facial attribute label representing identity-level prominent features on an existing face image dataset, CarVer, evaluating its performance against another facial attribute dataset, CelebA. Additionally, we expand CarVer with a set of images from internet sources, creating a new dataset we call CarVerX. This thesis analyzes the utility of identity-level prominent features for explainable face verification. We find that our prominent features outperform traditional facial attributes for the task of face verification opening the door for future research along this avenue for explainable face verification.

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

• Flourens, Cooper

Advisor dc:contributor.advisor

• Hand, Emily

Committee members dc:contributor.committeemember

• Tavakkoli, Alireza
• Young, Benjamin

• Attributes
• Explainability
• Face Verification
• Interpretability

Statement dc:rights

• Creative Commons Attribution-NonCommercial 4.0 International

Repository record dc:identifier.uri

https://scholarwolf.unr.edu/handle/11714/10960

OAI identifier oai:identifier

oai:scholarwolf.unr.edu:11714/10960