We present a model-based feature extractor to describe neighborhoods around
keypoints by finite expansion, estimating the spatially varying orientation by
harmonic functions. The iso-curves of such functions are highly symmetric
w.r.t. the origin (a keypoint) and the estimated parameters have well defined
geometric interpretations. The origin is also a unique singularity of all
harmonic functions, helping to determine the location of a keypoint precisely,
whereas the functions describe the object shape of the neighborhood. This is
novel and complementary to traditional texture features which describe
texture-shape properties i.e. they are purposively invariant to translation
(within a texture). We report on experiments of verification and identification
of keypoints in forensic fingerprints by using publicly available data (NIST
SD27) and discuss the results in comparison to other studies. These support our
conclusions that the novel features can equip single cores or single minutia
with a significant verification power at 19% EER, and an identification power
of 24-78% for ranks of 1-20. Additionally, we report verification results of
periocular biometrics using near-infrared images, reaching an EER performance
of 13%, which is comparable to the state of the art. More importantly, fusion
of two systems, our and texture features (Gabor), result in a measurable
performance improvement. We report reduction of the EER to 9%, supporting the
view that the novel features capture relevant visual information, which
traditional texture features do not.

通过模型化特征提取器描述关键点周围的邻域，使用有限展开来估计空间变化的方向，利用谐波函数来确定关键点位置并描述邻域的物体形状，这对传统纹理特征是一种新的补充和附加，具有显著的验证和识别能力。