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A comparative study on texture and surface descriptors for ear biometrics

机译：耳朵生物识别技术的纹理和表面描述符的比较研究

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Recent research in texture-based ear recognition also indicates that ear detection and texture-based ear recognition are robust against signal degradation and encoding artefacts. Based on these findings, we further investigate and compare the performance of texture descriptors for ear recognition and seek to explore possibilities to complement texture descriptors with depth information. On the basis of ear images from visible light and depth maps, we extract texture and surface descriptors. We compare the recognition performance of selected methods for describing texture and surface structure, which are Local Binary Patterns, Local Phase Quantization, Histograms of Oriented Gradients, Binarized Statistical Image Features, Shape Context and Curvedness. Secondly we propose a novel histogram-based descriptor that performs feature level fusion by combining two information channels to a new feature vector. Our concept can either be applied for fusing two different texture or two different surface descriptors or to combine texture and depth information. Based on the results of the previous experiment, we select the best performing configuration settings for texture and surface representation and use them as an input for our fused feature vectors. We report the performance of different variations of the fused descriptor and compare the behavior of the fused feature vectors with single channel from the first series of experiments.
机译：基于纹理的耳朵识别的最新研究还表明，耳朵检测和基于纹理的耳朵识别对于信号降级和编码伪像具有鲁棒性。基于这些发现，我们进一步研究和比较了纹理描述符在人耳识别方面的性能，并试图探索用深度信息补充纹理描述符的可能性。根据来自可见光和深度图的耳朵图像，我们提取纹理和表面描述符。我们比较了用于描述纹理和表面结构的选定方法的识别性能，这些方法是局部二值模式，局部相量化，定向梯度直方图，二值化统计图像特征，形状上下文和弯曲度。其次，我们提出了一种新颖的基于直方图的描述符，该描述符通过将两个信息通道组合到一个新的特征向量来执行特征级融合。我们的概念既可以应用于融合两个不同的纹理，也可以应用于两个不同的表面描述符，或者将纹理和深度信息相结合。根据先前实验的结果，我们为纹理和表面表示选择性能最佳的配置设置，并将其用作融合特征向量的输入。我们报告了融合描述符的不同变体的性能，并比较了来自第一批实验的融合特征向量与单个通道的行为。

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