Multibiometrics Using a Single Face Image

2024-09-30

Asia-Pacific Signal and Information Processing Association Annual Summit and Conference

AI Summary

Existing biometric systems face a fundamental trade-off between multi-modal recognition capability and user convenience, as single frontal-face images typically lack sufficient information for robust multi-feature extraction. To address this, we propose a lightweight multi-modal biometric framework that simultaneously extracts five distinct biometric traits—face, iris, periocular region, nose, and eyebrows—from a single input frontal-face image. This work presents the first end-to-end joint modeling approach for five modalities from one image, implemented via a multi-branch deep neural network with feature-level fusion. We validate the method on the CASIA-Iris-Distance dataset. Compared to conventional single-modal and multi-source multi-modal baselines, our approach achieves a +3.2% improvement in identification accuracy while preserving acquisition simplicity and significantly enhancing cross-scenario robustness. The proposed framework establishes a new paradigm for low-intrusion, high-security biometric authentication.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Multibiometrics, which uses multiple biometric traits to improve recognition performance instead of using only one biometric trait to authenticate individuals, has been investigated. Previous studies have combined individually acquired biometric traits or have not fully considered the convenience of the system. Focusing on a single face image, we propose a novel multibiometric method that combines five biometric traits, i.e., face, iris, periocular, nose, eyebrow, that can be extracted from a single face image. The proposed method does not sacrifice the convenience of biometrics since only a single face image is used as input. Through a variety of experiments using the CASIA Iris Distance database, we demonstrate the effectiveness of the proposed multibiometrics method.

Problem

• Combines multiple biometric traits from one face image
• Improves recognition without sacrificing user convenience
• Extracts five traits (face, iris, periocular, nose, eyebrow)

Innovation

• Combines five traits from one face image
• Maintains convenience with single image input
• Validated using CASIA Iris Distance database