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Iris recognition plays an important role in biometrics. Until now, many scholars have made different efforts in this field. However, the recognition performances of most proposed methods degrade dramatically when the image contains some noise, which inevitably occurs during image acquisition such as reflection spots, inconsistent illumination, eyelid, eyelash, hair, etc. In this paper, an accurate...
Human identification via multibiometrics is a very promising approach to improve overall systems security and recognition performance. In this paper, a novel multibiometrics fusion strategy based on dual iris, visible and thermal face traits is proposed. Firstly, 1D Log-Gabor dual iris codes are fused into a serial feature vector to address the noisy problems of iris. Then, Complex Gabor Jet Descriptor...
Recently, biometric template protection has received ever increasing research interests. However, most of the proposed schemes are not yet sufficiently mature for large scale deployment, they do not meet the requirements of security, and high-recognition performance. For example, matching methods based on Euclidean measurement ignore the difference of different component easily. Mean quantization...
A novel multimodal biometric recognition algorithm based on complex kernel fisher discriminant analysis (complex KFDA) is proposed. Complex KFDA exploits two phases to generalize KFDA and perform classification for the fusion feature set: complex KPCA plus complex LDA. As two distinct biometric modals, the features of iris and face are fused in parallel to test our algorithm. Experimental results...
As one of biometric technologies for personal identification and verification, many algorithm for iris recognition are proposed in recent decades. In these algorithms, iris templates are stored uncovered in the database. Once the database is compromised, the attackers can potentially obtain sufficient information to impersonate the users. This paper proposes a novel template protection algorithm for...
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