关键词: SURF算法, 关键点匹配, 模糊推理, 掌纹识别

Abstract: To alleviate the limitations that the existing palmprint recognition methods are time-consuming, and their robustness to the variations of orientation, position and illumination in capturing palmprint images is insufficient, this paper describes a new palmprint recognition method based on Speeded Up Robust Features（SURF） and fuzzy reasoning. The proposed method consists of two phases: enrolling phase and identification phase. In the enrolling phase, keypoints are detected with SURF from the enrolled palmprint images and mutually matched between training samples, and then for each enrolled user, the keypoints which matching frequencies exceed 50% are selected. For each selected keypoint, the matching rate, the mean and variance of the location coordinates and the mean of SURF descriptors in the enrolled palmprint images, the maximal distance between the mean and the SURF descriptors are determined and regarded as the user’s palmprint patterns stored in a pattern library. In the identification phase, it utilizes SURF to detect keypoints from the palmprint image to be identified, and then the fuzzy matching degrees for all palmprint patterns in the library are calculated. A fussy reasoning approach is developed for matching. Experimental results demonstrate that the proposed method yields a better performance in terms of the correct classification percentages compared with the recent palmprint recognition algorithms. It is also shown that the proposed approach is robust to the variations of orientation, position and illumination, and yields observably low computational cost.

Key words: Speeded Up Robust Features（SURF）, point matching, fuzzy reasoning, palmprint recognition