Selection of optimized features and weights on face-iris fusion using distance images

Abstract

The focus of this paper is on proposing new schemes based on score level and feature level fusion to fuse face and iris modalities by employing several global and local feature extraction methods in order to effectively code face and iris modalities. The proposed schemes are examined using different techniques at matching score level and feature level fusion on CASIA Iris Distance database, Print Attack face database, Replay Attack face database and IIIT-Delhi Contact Lens iris database. The proposed schemes involve the consideration of Particle Swarm Optimization (PSO) and Backtracking Search Algorithm (BSA) in order to select optimized features and weights to achieve robust recognition system by reducing the number of features in feature level fusion of the multimodal biometric system and optimizing the weights assigned to the face-iris multimodal biometric system scores in score level fusion step. Additionally, in order to improve face and iris recognition systems and subsequently the recognition of multimodal face-iris biometric system, the proposed methods attempt to correct and align the location of both eyes by measuring the iris rotation angle. Demonstration of the results based on both identification and verification rates clarifies that the proposed fusion schemes obtain a significant improvement over unimodal and other multimodal methods implemented in this study. Furthermore, the robustness of the proposed multimodal schemes is demonstrated against spoof attacks on several face and iris spoofing datasets.