Multiple deep convolutional neural networks averaging for face alignment

Shaohua Zhang; Hua Yang; Zhouping Yin

doi:10.1117/1.JEI.24.3.033013

26 May 2015 Multiple deep convolutional neural networks averaging for face alignment

Shaohua Zhang, Hua Yang, Zhouping Yin

Author Affiliations +

Journal of Electronic Imaging, Vol. 24, Issue 3, 033013 (May 2015). https://doi.org/10.1117/1.JEI.24.3.033013

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.

Citation Download Citation

Shaohua Zhang, Hua Yang, and Zhouping Yin "Multiple deep convolutional neural networks averaging for face alignment," Journal of Electronic Imaging 24(3), 033013 (26 May 2015). https://doi.org/10.1117/1.JEI.24.3.033013

Published: 26 May 2015

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available