Finger-vein recognition based on gradient distribution and self-adaptive recovery model

Resulting from nonuniform illumination and finger shift, current phalangeal joint locating methods cannot segment adequate vein pattern information and locate phalangeal joint reliably. Hence, we propose a robust gradient-based approach to detect phalangeal joint accurately and achieve sufficient valuable information. The detection accuracy is 98.23% and 99.32% in two datasets consisting of abnormal finger-vein images, respectively. Furthermore, the captured image contains noise and irregular shading inevitably, which can seriously affect further comprehensive study of finger-vein characteristics. Thus, we propose a fast and self-adaptive recovery model, which is built on atmospheric scattering model and finger-vein imaging principle, to extract finger-vein patterns reliably. Finally, a more reliable template matching method is used for finger-vein patterns matching. The resulting equal error rate is 2.95% and 2.83% in two databases, which consist of 1260 and 384 finger-vein images, respectively.