IdentiFace : A VGG Based Multimodal Facial Biometric System
Mahmoud Rabea, Hanya Ahmed, Sohaila Mahmoud, Nourhan Sayed

TL;DR
IdentiFace is a multimodal facial biometric system based on VGG-16 architecture that combines facial recognition with soft biometric traits like gender, face shape, and emotion, achieving high accuracy across multiple tasks.
Contribution
This paper introduces a unified VGG-16 based architecture for multimodal facial biometrics, simplifying integration and interpretation of features across different biometric traits.
Findings
99.2% accuracy in facial recognition on FERET data
99.4% gender recognition accuracy on our dataset
66.13% emotion recognition accuracy on FER2013
Abstract
The development of facial biometric systems has contributed greatly to the development of the computer vision field. Nowadays, there's always a need to develop a multimodal system that combines multiple biometric traits in an efficient, meaningful way. In this paper, we introduce "IdentiFace" which is a multimodal facial biometric system that combines the core of facial recognition with some of the most important soft biometric traits such as gender, face shape, and emotion. We also focused on developing the system using only VGG-16 inspired architecture with minor changes across different subsystems. This unification allows for simpler integration across modalities. It makes it easier to interpret the learned features between the tasks which gives a good indication about the decision-making process across the facial modalities and potential connection. For the recognition problem, we…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsFace recognition and analysis · Face and Expression Recognition · Biometric Identification and Security
