In this chapter, the problem of matching face against iris images using ocular information is considered. Face and iris images are typically acquired using different sensors: face recognition is predominantly conducted in the visible (VIS) spectrum while iris recognition is performed in the near-infrared (NIR) spectrum. Further, the subject-to-camera distance for face and iris recognition is substantially different. Due to these and other factors, the problem of matching face images against iris images is riddled with several challenges. To address this, we propose a novel matching algorithm based on Joint Dictionary-based Sparse Representation (JDSR) that exploits the use of ocular information available in both face and iris images. Experimental results on a database containing 1,358 images of 704 subjects indicate that the ocular region can provide better performance than the iris region in this challenging cross-modality matching scenario.

Chapter Contents:

• 17.1 Introduction
• 17.2 Database
• 17.2.1 Challenges
• 17.3 Outline of experiments
• 17.4 Iris recognition
• 17.4.1 Open source algorithm
• 17.4.1.1 Iris segmentation
• 17.4.2 Commercial algorithm
• 17.5 Ocular recognition
• 17.5.1 Baseline - local binary patterns
• 17.5.2 Normalized gradient correlation
• 17.6 Ocular matching using joint dictionary approach
• 17.6.1 Sparse representation framework
• 17.6.2 Joint dictionary approach
• 17.6.3 Dictionary learning and matching
• 17.7 Computational details
• 17.8 Score-level fusion
• 17.9 Summary
• References

Inspec keywords: iris recognition; infrared spectra; visible spectra; face recognition; image matching; visual databases; image representation

Other keywords: near-infrared spectrum; JDSR; visible spectrum; face image matching; VIS spectrum; cross-modality matching scenario; NIR spectrum; image database; subject-to-camera distance; iris image matching; ocular information; joint dictionary-based sparse representation scheme

Subjects: Spatial and pictorial databases; Computer vision and image processing techniques; Image recognition