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.

翻译：我们提出了一种基于模型的特征提取器，通过有限展开描述关键点邻域，并利用谐波函数估计空间变化的取向。此类函数的等值线相对于原点（关键点）高度对称，且估计参数具有明确的几何解释。原点同时是所有谐波函数的唯一奇点，有助于精确定位关键点位置，而谐波函数则描述了邻域的目标形状。这一特性具有创新性，且与描述纹理-形状属性的传统纹理特征互补——传统特征在纹理范围内具有平移不变性。我们利用公开数据集（NIST SD27）报告了法医指纹关键点验证与识别实验，并将结果与其他研究进行对比分析。实验支持以下结论：该新型特征可使单个核心点或单个细节点具备显著验证能力（等错误率19%），并实现1-20位次间24-78%的识别率。此外，我们报告了基于近红外图像的眼周生物特征验证结果，等错误率达13%，与当前最优水平相当。更重要的是，将本方法与纹理特征（Gabor）融合后，系统性能获得可量化的提升：等错误率降低至9%，验证了新型特征能捕获传统纹理特征所遗漏的相关视觉信息。