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2021 | OriginalPaper | Buchkapitel

FaceHop: A Light-Weight Low-Resolution Face Gender Classification Method

verfasst von : Mozhdeh Rouhsedaghat, Yifan Wang, Xiou Ge, Shuowen Hu, Suya You, C. -C. Jay Kuo

Erschienen in: Pattern Recognition. ICPR International Workshops and Challenges

Verlag: Springer International Publishing

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Abstract

A light-weight low-resolution face gender classification method, called FaceHop, is proposed in this research. We have witnessed rapid progress in face gender classification accuracy due to the adoption of deep learning (DL) technology. Yet, DL-based systems are not suitable for resource-constrained environments with limited networking and computing. FaceHop offers an interpretable non-parametric machine learning solution. It has desired characteristics such as a small model size, a small training data amount, low training complexity, and low-resolution input images. FaceHop is developed with the successive subspace learning (SSL) principle and built upon the foundation of PixelHop++. The effectiveness of the FaceHop method is demonstrated by experiments. For gray-scale face images of resolution \(32 \times 32\) in the LFW and the CMU Multi-PIE datasets, FaceHop achieves correct gender classification rates of 94.63% and 95.12% with model sizes of 16.9K and 17.6K parameters, respectively. It outperforms LeNet-5 in classification accuracy while LeNet-5 has a model size of 75.8K parameters.

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Titel: FaceHop: A Light-Weight Low-Resolution Face Gender Classification Method
verfasst von: Mozhdeh Rouhsedaghat
Yifan Wang
Xiou Ge
Shuowen Hu
Suya You
C. -C. Jay Kuo
Verlag: Springer International Publishing
Buch: Pattern Recognition. ICPR International Workshops and Challenges
Print ISBN: 978-3-030-68792-2

Electronic ISBN: 978-3-030-68793-9

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-68793-9_12

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