Expected Confusion as a Method of Evaluating Recognition Techniques
Bobick, Aaron F.
Johnson, Amos Y., Jr.
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We derive an expected confusion metric, as opposed to reporting percent correct with a limited database, as a method to evaluate recognition techniques. This metric allows us to predict how well a given feature vector will filter identity in a large population. Our expected confusion is the ratio of the average individual variation of a feature vector. We evaluate our gait-recognition technique that recovers static body and stride parameters of walking subjects with the expected confusion metric to demonstrate its use