Abstract
This Letter proposes automatic human face detection in digital video using a support vector machine (SVM) ensemble to improve the detection performance. The SVM ensemble consists of several independently trained SVMs using randomly chosen training samples via a bootstrap technique. Next, they are aggregated in order to make a collective decision via a majority voting scheme. Experimental results show that the proposed face detection method using SVM ensemble outperforms conventional methods such as using only single SVM and Multi-Layer Perceptron in terms of classification accuracy, false alarms, and missing rates.
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Je, HM., Kim, D. & Yang Bang, S. Human Face Detection in Digital Video Using SVMEnsemble. Neural Processing Letters 17, 239–252 (2003). https://doi.org/10.1023/A:1026097128675
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DOI: https://doi.org/10.1023/A:1026097128675