20 March 2017 Age and gender classification in the wild with unsupervised feature learning
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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.
© 2017 SPIE and IS&T
Lihong Wan, Lihong Wan, Hong Huo, Hong Huo, Tao Fang, 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 . Submission: Received: 15 October 2016; Accepted: 20 February 2017
Received: 15 October 2016; Accepted: 20 February 2017; Published: 20 March 2017

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