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32 lines
1.6 KiB
32 lines
1.6 KiB
This implements a face alignment method, as preprocessing to tasks such as age and gender estimation,
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and face recognition as described in [1] and [2].
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The code calls an executable of facial landmarks detection, by X.Zhu and D. Ramanan, implementing the algorithm described in [3].
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(The rest of this text is a quotation from their code: Copyright (C) 2012 Xiangxin Zhu, Deva Ramanan)
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It includes pre-trained face models.
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Much of the detection code is built on top of part-based model implementation of [4].
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The training code implements a quadratic program (QP) solver described in [5].
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In the training code, we use the positive samples from MultiPIE dataset (available at www.multipie.org) and the negative images from the INRIAPerson dataset [6] (included in the package).
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Acknowledgements: We graciously thank the authors of the previous code releases and image benchmarks for making them publicly available.
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References
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==========
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[1] E. Eidinger, R. Enbar, T. Hassner, Age and Gender Estimation of Unfiltered Faces, submitted to IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2014
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[2] http://www.openu.ac.il/home/hassner/Adience/links.html
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[3] X. Zhu, D. Ramanan. Face Detection, Pose Estimation and Landmark Localization in the Wild. CVPR 2012.
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[4] P. Felzenszwalb, R. Girshick, D. McAllester. Discriminatively Trained Deformable Part Models. http://people.cs.uchicago.edu/~pff/latent.
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[5] D. Ramanan. Dual Coordinate Descent Solvers for Large Structured Prediction Problems. UCI Technical Report, to appear.
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[6] N. Dalal, B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005.
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