Abstract
This paper aims at detecting faces with all -/+90-degree rotation-out-of-plane and 360-degree rotation-in-plane pose changes fast and accurately under embedded hardware environment. We present a fine-classified method and a hardware architecture for rotation invariant multi-view face detection. A tree-structured detector hierarchy is designed to organize multiple detector nodes identifying pose ranges of faces. We propose a boosting algorithm for training the detector nodes. The strong classifier in each detector node is composed of multiple novelly-designed two-stage weak classifiers. Each detector node deals with the multi-dimensional binary classification problems by means of a shared output space of multi-components vector. The characteristics of the proposed method is analyzed for fully exploiting the spatial and temporal parallelism. We present the design of the hardware architecture in detail. Experiments on FPGA show that high accuracy and amazing speed are achieved compared with previous related works. The execution time speedups are significant when our FPGA design is compared with software solution on PC.
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Xu, J., Dou, Y., Tang, Y., Wang, X. (2009). Implementation of Rotation Invariant Multi-View Face Detection on FPGA. In: Dou, Y., Gruber, R., Joller, J.M. (eds) Advanced Parallel Processing Technologies. APPT 2009. Lecture Notes in Computer Science, vol 5737. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03644-6_7
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DOI: https://doi.org/10.1007/978-3-642-03644-6_7
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