Analysis of performance of palmprint matching with enforced sparsity

In this paper, a new and simple palmprint recognition solution based on sparse representation is suggested. It is shown that when the aim is to recover a palmprint from a limited number of observations as a linear combination of measurements of the same palmprint class, the ensuing representation in intrinsically very sparse. It can be efficiently computed by solving an l1 norm convex minimisation problem. When combined with well known subspace feature selection techniques such as PCA and LDA as well as with downsampled images, our tests, which have been carried out on 250 classes of the widely used PolyU database, have yielded an EER as low as 0.11% depending on the palmprints selected during the enrolment phase. Coupled with an execution time as short as 8.4 ms, the obtained results outperform similar work in the literature including EigenPalms, FisherPalms and Gabor based palmprint matching algorithms, which shows the effectiveness of the new solution.