Existing convolutional neural network (CNN) based face recognition algorithms typically learn a discriminative feature mapping, using a loss function that enforces separation of features from different classes and/or aggregation of features within the same class. However, they may suffer from bias in the training data such as uneven sampling density, because they optimize the adjacency relationship of the learned features without considering the proximity of the underlying faces. Moreover, since they only use facial images for training, the learned feature mapping may not correctly indicate the relationship of other attributes such as gender and ethnicity, which can be important for some face recognition applications. In this paper, we propose a new CNN-based face recognition approach that incorporates such attributes into the training process. Using an attribute-aware loss function that regularizes the feature mapping using attribute proximity, our approach learns more discriminative features that are correlated with the attributes. We train our face recognition model on a large-scale RGB-D data set with over 100K identities captured under real application conditions. By comparing our approach with other methods on a variety of experiments, we demonstrate that depth channel and attribute-aware loss greatly improve the accuracy and robustness of face recognition.

3D face reconstruction from a single image is a classical and challenging problem, with wide applications in many areas. Inspired by recent works in face animation from RGBD or monocular video inputs, we develop a novel method for reconstructing 3D faces from unconstrained 2D images, using a coarse-to-fine optimization strategy. First, a smooth coarse 3D face is generated from an example-based bilinear face model, by aligning the projection of 3D face landmarks with 2D landmarks detected from the input image. Afterwards, using local corrective deformation fields, the coarse 3D face is refined using photometric consistency constraints, resulting in a medium face shape. Finally, a shape-from-shading method is applied on the medium face to recover fine geometric details. Our method outperforms stateof- the-art approaches in terms of accuracy and detail recovery, which is demonstrated in extensive experiments using real world models and publicly available datasets.

Scissor structure is used to generate deployable objects for space-saving in a variety of applications, from architecture to aerospace science. While deployment from a small, regular shape to a larger one is easy to design, we focus on a more challenging task: designing a planar scissor structure that deploys from a given source shape into a specific target shape. We propose a two-step constructive method to generate a scissor structure from a high-dimensional parameter space. Topology construction of the scissor structure is first performed to approximate the two given shapes, as well as to guarantee the deployment. Then the geometry of the scissor structure is optimized in order to minimize the connection deflections and maximize the shape approximation. With the optimized parameters, the deployment can be simulated by controlling an anchor scissor unit. Physical deployable objects are fabricated according to the designed scissor structures by using 3D printing or manual assembly. We show a number of results for different shapes to demonstrate that even with fabrication errors, our designed structures can deform fluently between the source and target shapes.