Effects of severe signal degradation on ear detection

Ear recognition has recently gained much attention, as for surveillance scenarios identification remains feasible, in case the facial characteristic is partly or fully covered. However video footage stemming from surveillance cameras is often of low quality. In this work we investigate the impact of signal degradation, i.e. out-of-focus blur and thermal noise, on the segmentation accuracy of automated ear detection. Realistic acquisition scenarios are constructed and various intensities of signal degradation are simulated on a comprehensive dataset. In experiments different ear detection algorithms are employed, pointing out the effects of severe signal degradation on ear segmentation performance.