Efficient high-speed framework for sparse representation-based iris recognition

While various frameworks for iris recognition have been proposed, most lack efficiency and high speed. A new framework for iris recognition is presented that is both efficient and fast. Feature extraction is performed by extracting Gabor features and then applying supervised locality-preserving projections with heat kernel weights, which improves the recognition rate in comparison with the results from unsupervised dimensionality reduction techniques such as principal component analysis, locality-preserving projections, and random projections. Afterwards, a classification is performed using the recently proposed sparse representation-based classification (SRC). To considerably improve classification performance, SRC is proposed, using a greedy compressed-sensing recovery algorithm, as opposed to employing the traditional computationally expensive l(1) minimisation. The proposed framework achieves a recognition rate of about 99.5% using two iris databases, with a significant improvement in speed over related frameworks.

Automated summary

A new framework for iris recognition with high efficiency and speed is presented, utilizing Gabor features and supervised locality-preserving projections with heat kernel weights for feature extraction, as well as sparse representation-based classification to significantly improve recognition rate and performance.