Abstract
For the past decades, recognition technologies of multispectral palmprint have attracted more and more attention due to their abundant spatial and spectral characteristics compared with the single spectral case. Enlightened by this, an innovative robust L2 sparse representation with tensor-based extreme learning machine (RL2SR-TELM) algorithm is put forward by using an adaptive image level fusion strategy to accomplish the multispectral palmprint recognition. Firstly, we construct a robust L2 sparse representation (RL2SR) optimization model to calculate the linear representation coefficients. To suppress the affection caused by noise contamination, we introduce a logistic function into RL2SR model to evaluate the representation residual. Secondly, we propose a novel weighted sparse and collaborative concentration index (WSCCI) to calculate the fusion weight adaptively. Finally, we put forward a TELM approach to carry out the classification task. It can deal with the high dimension data directly and reserve the image spatial information well. Extensive experiments are implemented on the benchmark multispectral palmprint database provided by PolyU. The experiment results validate that our RL2SR-TELM algorithm overmatches a number of state-of-the-art multispectral palmprint recognition algorithms both when the images are noise-free and contaminated by different noises.
Highlights
Palmprint recognition technologies have become a novel biometric approach and have attracted increasingly attention in recent years
The sparse representation idea was introduced into the biometric recognition for first time d×nthe
For the sake of demonstrating the robustness of the presented robust L2 sparse representation (RL2SR) model, we we accomplish experiments compared with several differentmodels, models,such suchasasSRC, sparse representation-based classification (SRC),collaborative representation-based classification (CRC)
Summary
Palmprint recognition technologies have become a novel biometric approach and have attracted increasingly attention in recent years. Xu et al [39] presented a novel multispectral palmprint recognition algorithm They used the digital shearlet transform (DST) to implement the image fusion and proposed a multiclass projection ELM (MPELM) to accomplish the classification task. How to increase the recognition accuracy when the collected images are contaminated by different noises Inspired by the these studes, in this article, we present a novel robust L2 sparse representation with a tensor-based extreme learning machine (RL2SR-TELM) algorithm by using an adaptive image. Sensors 2019, 19, x FOR PEER REVIEW our algorithm can be to summarized follows: Firstly,palmprint a robust L2 norm-based sparse representation level fusion strategy accomplishas the multispectral recognition.
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