ear, modality, science

We’re all ears

New academic research on ear biometrics…

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.

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