Abstract
Face detection is a time consuming task in computer vision applications. In this article, an approach for AdaBoost face detection using Haar-like features on the GPU is proposed. The GPU adapted version of the algorithm manages to speed-up the detection process when compared with the detection performance of the CPU using a well-known computer vision library. An overall speed-up of × 3.3 is obtained on the GPU for video resolutions of 640x480 px when compared with the CPU implementation. Moreover, since the CPU is idle during face detection, it can be used simultaneously for other computer vision tasks.
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Martínez-Zarzuela, M., Díaz-Pernas, F.J., Antón-Rodríguez, M., Perozo-Rondón, F., González-Ortega, D. (2011). AdaBoost Face Detection on the GPU Using Haar-Like Features. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds) New Challenges on Bioinspired Applications. IWINAC 2011. Lecture Notes in Computer Science, vol 6687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21326-7_36
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DOI: https://doi.org/10.1007/978-3-642-21326-7_36
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