Abstract
This paper investigates how to recognize faces with partial occlusions using iterative stacked denoising autoencoder (ISDAE). We introduce a mapping-autoencoder (MAE) for occlusion detection, which requires no prior knowledge of occlusion. Inspired by stacked denoising autoencoder (SDAE)’s capability to learn patterns from noisy data, we propose a novel iterative structure of SDAE for occluded faces restoration. Deep neural network (DNN) is used for final recognition. Compared with the state-of-the-art approaches (e.g. sparse representation), ISDAE achieves competitive results under serious occlusion conditions.
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Zhang, Y., Liu, R., Zhang, S., Zhu, M. (2013). Occlusion-Robust Face Recognition Using Iterative Stacked Denoising Autoencoder. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_44
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DOI: https://doi.org/10.1007/978-3-642-42051-1_44
Publisher Name: Springer, Berlin, Heidelberg
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