CREDIT.TXT

This implements a face alignment method, as preprocessing to tasks such as age and gender estimation, 
and face recognition as described in [1] and [2].

The code calls an executable of facial landmarks detection, by X.Zhu and D. Ramanan, implementing the algorithm described in [3]. 

(The rest of this text is a quotation from their code: Copyright (C) 2012 Xiangxin Zhu, Deva Ramanan)

It includes pre-trained face models. 

Much of the detection code is built on top of part-based model implementation of [4]. 

The training code implements a quadratic program (QP) solver described in [5].

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). 

Acknowledgements: We graciously thank the authors of the previous code releases and image benchmarks for making them publicly available.

References
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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

[2] http://www.openu.ac.il/home/hassner/Adience/links.html

[3] X. Zhu, D. Ramanan. Face Detection, Pose Estimation and Landmark Localization in the Wild. CVPR 2012.

[4] P. Felzenszwalb, R. Girshick, D. McAllester. Discriminatively Trained Deformable Part Models. http://people.cs.uchicago.edu/~pff/latent.

[5] D. Ramanan. Dual Coordinate Descent Solvers for Large Structured Prediction Problems. UCI Technical Report, to appear.

[6] N. Dalal, B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005.