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Downsampling in uniformly-spaced windows for coding-based Palmprint recognition
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AI
DOI：10.1007/s11042-023-14574-z.png)
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Abstract
En 中文
Palmprint is deemed as one of the most important biometric modalities. Many coding-based palmprint recognition methods have achieved satisfactory recognition performance, which can be free from training and require low storage cost and computational complexity. Downsampling is typically used to improve real-time ability, reduce the storage cost and improve the discriminative ability. Unfortunately, downsampling was not fully considered and studied. In this paper, we propose downsampling in uniformly-spaced windows (DUSW) and conduct it on two state-of-the-art downsampling methods as their reformative versions, dubbed uniformly-spaced extreme downsampling method (U-EDM) and uniformly-spaced democratic voting downsampling method (U-DVDM). In DUSW, the upper-left four pixels rather than all pixels in each block are selected to jointly decide the winner whose value is used as the representative feature of this block. DUSW overcomes the dictatorship of a single pixel and simultaneously ensures the sufficient spatial distance between the adjacent winners. Thus, DUSW reduces the correlation between the adjacent winners, and accordingly improves the discrimination and robustness. Meanwhile, the computational complexity is only 1/4 of the original downsampling methods for the representative reduction. The sufficient experiments demonstrate that DUSW can be easily embedded into the existing downsampling methods of coding-based palmprint recognition and improve their recognition performances.
Keywords:
Downsampling in uniformly-spaced windows
Uniformly-spaced extreme downsampling method
Uniformly-spaced democratic voting downsampling method
Palmprint recognition
Coding-based palmprint recognition method
Downsampling
Journal
IF:
3
Papers:
1.9W
Citations:
3.2W