The principal component analysis network (PCANet) is asserted as a parsimonious stacking-based convolutional neural networks (CNNs) instance for generic object recognition including face. However, to be regarded a CNN resemblance, PCANet lacks a nonlinearity in between two successive convolutional layers. The multilayer PCANet (by neglecting the nonlinearity pre-requisite) is also deemed far-fetched for the network depth beyond two, due to feature dimensionality explosion. We thus devise a PCANet alternative, dubbed PCANet+ in this letter, to untangle these constraints. To be more precise, conforming to the CNN essentials, PCANet+ conveys a mean-pooling unit manipulating each feature map. On top of that, we streamline the PCANet topology to permit a deep construction with an expanded PCA filter ensemble. We scrutinize the PCANet+ performance using face recognition technology and other two faces in the wild datasets, namely, labeled faces in the wild and YouTube faces. The experimental results reveal that the PCANet+ descriptor prevails over its predecessor and other stacking-based descriptors in face identification and verification, serving a baseline for ConvNets.
Bibliographical noteFunding Information:
Manuscript received July 13, 2017; revised August 23, 2017; accepted August 31, 2017. Date of publication September 7, 2017; date of current version September 21, 2017. This work was supported by the National Research Foundation of Korea funded by the Korea government (Ministry of Science, ICT and Future Planning) under Grant 2016R1A2B4011656. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Vishal Monga. (Corresponding author: Andrew Beng-Jin Teoh.) The authors are with the School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul 120749, South Korea (e-mail: firstname.lastname@example.org; email@example.com; firstname.lastname@example.org).
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All Science Journal Classification (ASJC) codes
- Signal Processing
- Electrical and Electronic Engineering
- Applied Mathematics