A comparative study on texture and surface descriptors for ear biometrics

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.