A biometric recognition system can operate in two distinct modes,
identification or verification. In the first mode, the system recognizes an
individual by searching the enrolled templates of all the users for a match. In
the second mode, the system validates a claimed identity by comparing the fresh
template with the enrolled template for this identity. Both the experimentally
determined false match rate and false non-match rate through recognition
threshold adjustment define the recognition accuracy, and hence the security of
the system. The biometric transformation schemes usually produce binary
templates that are better handled by cryptographic schemes. One of the
requirements for these transformation schemes is their irreversibility. In this
work, we rely on probabilistic modelling to quantify the security strength of
binary templates. We investigate the influence of template size, database size
and threshold on the probability of having a near-collision, and we highlight
two attacks on biometric systems. We discuss the choice of parameters through
the generic presented attacks.

这篇论文研究了基于概率建模的生物识别技术中的安全问题，探讨了模板大小、数据库大小和阈值对识别精度的影响，揭示了生物识别技术的两种攻击方式，并提出了选择参数的建议。

生物识别中的非定向近碰撞攻击

Untargeted Near-collision Attacks in Biometric Recognition

Fingerprint-based identification systems achieve higher accuracy when a slap
containing multiple fingerprints of a subject is used instead of a single
fingerprint. However, segmenting or auto-localizing all fingerprints in a slap
image is a challenging task due to the different orientations of fingerprints,
noisy backgrounds, and the smaller size of fingertip components. The presence
of slap images in a real-world dataset where one or more fingerprints are
rotated makes it challenging for a biometric recognition system to localize and
label the fingerprints automatically. Improper fingerprint localization and
finger labeling errors lead to poor matching performance. In this paper, we
introduce a method to generate arbitrary angled bounding boxes using a deep
learning-based algorithm that precisely localizes and labels fingerprints from
both axis-aligned and over-rotated slap images. We built a fingerprint
segmentation model named CRFSEG (Clarkson Rotated Fingerprint segmentation
Model) by updating the previously proposed CFSEG model which was based on
traditional Faster R-CNN architecture [21]. CRFSEG improves upon the Faster
R-CNN algorithm with arbitrarily angled bounding boxes that allow the CRFSEG to
perform better in challenging slap images. After training the CRFSEG algorithm
on a new dataset containing slap images collected from both adult and children
subjects, our results suggest that the CRFSEG model was invariant across
different age groups and can handle over-rotated slap images successfully. In
the Combined dataset containing both normal and rotated images of adult and
children subjects, we achieved a matching accuracy of 97.17%, which
outperformed state-of-the-art VeriFinger (94.25%) and NFSEG segmentation
systems (80.58%).

本文提出了一种基于深度学习的算法，用于精确定位和标记斜向和过度旋转的掌纹影像中的指纹，并通过在新数据集上的训练证明此算法在不同年龄组的掌纹影像中均具有不变性，并在成年人和儿童主体的正常和旋转图像的综合数据集中，实现了 97.17％的匹配准确度，表现优于现有的系统。