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Abstract:
This paper develops a new approach for extremely fast
detection in domains where the distribution of positive and
negative examples is highly skewed (e.g. face detection or
database retrieval). In such domains a cascade of simple
classifiers each trained to achieve high detection rates and
modest false positive rates can yield a final detector with many
desirable features: including high detection rates, very low
false positive rates, and fast performance. Achieving extremely
high detection rates, rather than low error, is not a task
typically addressed by machine learning algorithms. We propose a
new variant of AdaBoost as a mechanism for training the simple
classifiers used in the cascade. Experimental results in the
domain of face detection show the training algorithm yields
significant improvements in performance over conventional
AdaBoost. The final face detection system can process 15 frames
per second, achieves over 90% detection, and a false positive
rate of 1 in a 1,000,000.
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