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Journal of Electronic Imaging

Multiple deep convolutional neural networks averaging for face alignment
Author(s): Shaohua Zhang; Hua Yang; Zhouping Yin
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Paper Abstract

Face alignment is critical for face recognition, and the deep learning-based method shows promise for solving such issues, given that competitive results are achieved on benchmarks with additional benefits, such as dispensing with handcrafted features and initial shape. However, most existing deep learning-based approaches are complicated and quite time-consuming during training. We propose a compact face alignment method for fast training without decreasing its accuracy. Rectified linear unit is employed, which allows all networks approximately five times faster convergence than a tanh neuron. An eight learnable layer deep convolutional neural network (DCNN) based on local response normalization and a padding convolutional layer (PCL) is designed to provide reliable initial values during prediction. A model combination scheme is presented to further reduce errors, while showing that only two network architectures and hyperparameter selection procedures are required in our approach. A three-level cascaded system is ultimately built based on the DCNNs and model combination mode. Extensive experiments validate the effectiveness of our method and demonstrate comparable accuracy with state-of-the-art methods on BioID, labeled face parts in the wild, and Helen datasets.

Paper Details

Date Published: 26 May 2015
PDF: 13 pages
J. Electron. Imaging. 24(3) 033013 doi: 10.1117/1.JEI.24.3.033013
Published in: Journal of Electronic Imaging Volume 24, Issue 3
Show Author Affiliations
Shaohua Zhang, Huazhong Univ. of Science and Technology (China)
Hua Yang, Huazhong Univ. of Science and Technology (China)
Zhouping Yin, Huazhong Univ. of Science and Technology (China)

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