On improving robustness of LDA and SRDA by using tangent vectors

This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters, [Volume 34, Issue 9, 1 July 2013, Pages 1094–1100] DOI: 10.1016/j.patrec.2013.03.001[EN] In the area of pattern recognition, it is common for few training samples to be available with respect to the dimensionality of the representation space; this is known as the curse of dimensionality. This problem can be alleviated by using a dimensionality reduction approach, which overcomes the curse relatively well. Moreover, supervised dimensionality reduction techniques generally provide better recognition performance; however, several of these tend to suffer from the curse when applied directly to high-dimensional spaces. We propose to overcome this problem by incorporating additional information to supervised subspace learning techniques using what is known as tangent vectors. This additional information accounts for the possible differences that the sample data can suffer. In fact, this can be seen as a way to model the unseen data and make better use of the scarce training samples. In this paper, methods for incorporating tangent vector information are described for one classical technique (LDA) and one state-of-the-art technique (SRDA). Experimental results confirm that this additional information improves performance and robustness to known transformations.Work partially supported through the EU 7th Framework Programme grant tranScriptorium (Ref: 600707), by the Spanish MEC under the STraDA research project (TIN2012-37475-C02-01) and by the Generalitat Valenciana under grant Prometeo/2009/014.Villegas Santamaría, M.; Paredes Palacios, R. (2013). On improving robustness of LDA and SRDA by using tangent vectors. Pattern Recognition Letters. 34(9):1094-1100. https://doi.org/10.1016/j.patrec.2013.03.0011094110034

End-to-end 3D face reconstruction with deep neural networks

Monocular 3D facial shape reconstruction from a single 2D facial image has been an active research area due to its wide applications. Inspired by the success of deep neural networks (DNN), we propose a DNN-based approach for End-to-End 3D FAce Reconstruction (UH-E2FAR) from a single 2D image. Different from recent works that reconstruct and refine the 3D face in an iterative manner using both an RGB image and an initial 3D facial shape rendering, our DNN model is end-to-end, and thus the complicated 3D rendering process can be avoided. Moreover, we integrate in the DNN architecture two components, namely a multi-task loss function and a fusion convolutional neural network (CNN) to improve facial expression reconstruction. With the multi-task loss function, 3D face reconstruction is divided into neutral 3D facial shape reconstruction and expressive 3D facial shape reconstruction. The neutral 3D facial shape is class-specific. Therefore, higher layer features are useful. In comparison, the expressive 3D facial shape favors lower or intermediate layer features. With the fusion-CNN, features from different intermediate layers are fused and transformed for predicting the 3D expressive facial shape. Through extensive experiments, we demonstrate the superiority of our end-to-end framework in improving the accuracy of 3D face reconstruction.Comment: Accepted to CVPR1