An ear recognition system represents a powerful tool in forensic applications. Even in case the facial characteristic of a suspect is partly or fully covered an image of the outer ear may suffice to reveal a subject's identity. In forensic scenarios imagery may stem from surveillance cameras of environments where image compression is common practice to overcome limitations of storage or transmission capacities. Yet, the impact of severe image compression on ear recognition has remained undocumented. In this work the authors analyse the influence of different state-of-the-art image compression standards on ear detection and ear recognition algorithms. Evaluations conducted on an uncompressed ear database are considered with respect to different stages in the processing chain of an ear recognition system where compression may be applied, representing the most relevant forensic scenarios. Experimental results are discussed in detail highlighting the potential and limitations of automated ear recognition in presence of image compression.