Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Introduction to Biometrics
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This paper proposes an efficient algorithm for personal identification with biometric images. In identification based on image comparison, the number of comparisons is an important factor to estimate the total processing time in addition to the processing time of a single comparison. Maeda et al. proposed an identification algorithm that reduces the number of comparisons from the linear search algorithm, however the processing time of each comparison is proportional to the number of registered images. The algorithm in this paper is an improvement of the algorithm by Maeda et al. with constant-time image comparisons. This paper evaluates the algorithms in terms of the processing time and the accuracy with practical palmprint images, and proves that the novel algorithm can reduce the number of image comparisons from the linear search algorithm as the algorithm by Maeda et al. without loss of the accuracy.