摘要：Driven from its uniqueness, immutability, acceptability, and low cost, fingerprint is in a forefront betweenbiometric traits. Recently, the GPU has been considered as a promising parallel processing technology dueto its high performance computing, commodity, and availability. Fingerprint authentication is keep grow-ing, and includes the deployment of many image processing and computer vision algorithms. This paperintroduces the fingerprint local invariant feature extraction using two dominant detectors, namely SIFT andSURF, which are running on the CPU and the GPU. The paper focuses on the consumed time as an im-portant factor for fingerprint identification. The experimental results show that the GPU implementationsproduce promising behaviors for both SIFT and SURF compared to the CPU one. Moreover, the SURFfeature detector provides shorter processing time compared to the SIFT CPU and GPU implementations
关键词：biometrics; fingerprint images; processing time; SIFT; SURF; GPU; CUDA
