3D Ear Identification Based on Sparse Representation (Forensic Magazine)
Compared with classical biometric identifiers such as fingerprint and face, the ear is relatively a new member in the biometrics family and has recently received some significant attention due to its non-intrusiveness and ease of data collection. As a biometric identifier, the ear is appealing and has some desirable properties such as universality, uniqueness and permanence. The ear has a rich structure and a distinct shape which remains unchanged from 8 to 70 years of age as determined by Iannarelli in a study of 10,000 ears. The recognition using 2D ear images has a comparable discriminative power compared with the recognition using 2D face images.If you click through to the whole study at plos.org, the authors (Lin Zhang, Zhixuan Ding, Hongyu Li & Ying Shen) have made the Matlab source code for the ear matching algorithm available. That's really neat.
From our first post on ear biometrics in 2010...
Pros:
-Facial recognition accuracy is degraded as the pose angle diverges from a full frontal view. As pose angles get bigger, an ear will come into view. Tying an ear-recognition system to a face recognition system could make more identifications possible, especially with a non-participating subject.
Cons:
-Ears aren't really that stable. They grow throughout life, as the quote above addresses.
-As high school wrestlers can attest, ears are easily deformed by trauma.
-Hair obscures significant portions of the ear in a significant percentage of the population.
