Longitudinal Finger Rotation—Deformation Detection and Correction

Abstract

Finger vein biometrics is becoming more and more popular. However, longitudinal finger rotation, which can easily occur in practical applications, causes severe problems as the resulting vein structure is deformed in a non-linear way. These problems will become even more important in the future, as finger vein scanners are evolving toward contact-less acquisition. This paper provides a systematic evaluation regarding the influence of longitudinal rotation on the performance of finger vein recognition systems and the degree to which the deformations can be corrected. It presents two novel approaches to correct the longitudinal rotation, one based on the known rotation angle. The second one compensates the rotational deformation by applying a rotation correction in both directions using a pre-defined angle combined with score level fusion and works without any knowledge of the actual rotation angle. During the experiments, the aforementioned approaches and two additional are applied: one correcting the deformations based on an analysis of the geometric shape of the finger and the second one applying an elliptic pattern normalization of the region of interest. The experimental results confirm the negative impact of longitudinal rotation on the recognition performance and prove that its correction noticeably improves the performance again.

Finger Vein Template Protection Based on Alignment-Robust Feature Description and Index-of-Maximum Hashing

“…For the UTFVP a larger amount of displacement, e.g. longitudinal rotation is reported by [36]. This seems to be the reason for the performance issues concerning UTFVP as compared to the PLUSVein-FV3.…”

State of the Art in Vascular Biometrics

“…Techniques for RoI determination are typically described in the context of descriptions of the entire recognition toolchain. There are hardly papers dedicated to this [76,94,163,203,209,299] Binary vascular structure using semantic segmentation CNNs [91,[100][101][102] Minutiae [84,148,293] issue separately. A typical example is [287], where an inter-phalangeal joint prior is used for finger vein RoI localisation and haze removal methods with the subsequent application of Gabor filters are used for improving visibility of the vascular structure.…”

“…The corresponding patches are identified using mated SIFT key points. Longitudinal rotation correction in both directions using a predefined angle combined with scorelevel fusion is proposed and successfully applied in [203].…”

“…A topic of current intensive research is template comparison techniques (and suited feature representations) enabling the compensation of finger rotation and finger deformation [76,94,163,203,204,299]. Somewhat related is the consideration multi-perspective finger vein recognition, where two [153] and multiple [205] perspectives are fused to improve recognition results of single-perspective schemes.…”

Finger-Vein Pattern Restoration With Generative Adversarial Network

“…In these approaches, the vein network is segmented and stored in a binary image and then the minutiae features are extracted from resulting binary image. Unfortunately, finger-vein image quality is inherently affected by a number of factors: environmental illumination [17], [18], [19], ambient temperature [4], [19], [20], light scattering in imaging finger tissues [21], [22], [23], [24]. These factors are impossible to be controlled and/or avoided in practical applications, so the vein verification system generates some low quality images where the separability between the vein and non-vein patterns is poor or some vein patterns are corrupted.…”