Age and gender classification in the wild with unsupervised feature learning

Lihong Wan; Hong Huo; Tao Fang

doi:10.1117/1.JEI.26.2.023007

20 March 2017 Age and gender classification in the wild with unsupervised feature learning

Lihong Wan, Hong Huo, Tao Fang

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 2, 023007 (March 2017). https://doi.org/10.1117/1.JEI.26.2.023007

Abstract

Inspired by unsupervised feature learning (UFL) within the self-taught learning framework, we propose a method based on UFL, convolution representation, and part-based dimensionality reduction to handle facial age and gender classification, which are two challenging problems under unconstrained circumstances. First, UFL is introduced to learn selective receptive fields (filters) automatically by applying whitening transformation and spherical k-means on random patches collected from unlabeled data. The learning process is fast and has no hyperparameters to tune. Then, the input image is convolved with these filters to obtain filtering responses on which local contrast normalization is applied. Average pooling and feature concatenation are then used to form global face representation. Finally, linear discriminant analysis with part-based strategy is presented to reduce the dimensions of the global representation and to improve classification performances further. Experiments on three challenging databases, namely, Labeled faces in the wild, Gallagher group photos, and Adience, demonstrate the effectiveness of the proposed method relative to that of state-of-the-art approaches.

Citation Download Citation

Lihong Wan, Hong Huo, and Tao Fang "Age and gender classification in the wild with unsupervised feature learning," Journal of Electronic Imaging 26(2), 023007 (20 March 2017). https://doi.org/10.1117/1.JEI.26.2.023007

Received: 15 October 2016; Accepted: 20 February 2017; Published: 20 March 2017

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