Previous works have shown that the ear is a good candidate for a human biometric. However, in prior work, the pre-processing of ear images has been a manual process. Also, prior algorithms were sensitive to noise in the data, especially that caused by hair and earrings. We present a novel solution to the automated cropping of the ear and implement it in an end-to-end solution for biometric recognition. We demonstrate our automatic recognition process with the largest study to date in ear biometrics, 415 subjects, achieving a rank one recognition rate of 97.6%. This work represents a breakthrough in ear biometrics and paves the way for commercial quality, fully automatic systems.