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This paper explores hard and soft biometric systems. Hard biometrics is features that are used to uniquely identify individuals over time, while soft biometrics do not uniquely identify individuals and may not persist in the same state over an extended time. It develops methods that enable recognition using 2D ear images. This recognition is performed using a dataset which contains various lighting and pose conditions, as well as time lapse. It explores the growing field of ensemble biometrics, which subdivide a biometric feature into parts, and combine the results of several parts to yield recognition results.
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