The effects of radial lens distortion often appear in wide-angle cameras of surveillance and safeguard systems, which may severely degrade performances of previous face recognition algorithms. Traditional methods for radial lens distortion correction usually employ line features in scenarios that are not suitable for face images. In this paper, we propose a distortion-invariant face recognition system called RDCFace, which directly and only utilize the distorted images of faces, to alleviate the effects of radial lens distortion. RDCFace is an end-to-end trainable cascade network, which can learn rectification and alignment parameters to achieve a better face recognition performance without requiring supervision of facial landmarks and distortion parameters. We design sequential spatial transformer layers to optimize the correction, alignment, and recognition modules jointly. The feasibility of our method comes from implicitly using the statistics of the layout of face features learned from the large-scale face data. Extensive experiments indicate that our method is distortion robust and gains significant improvements on LFW, YTF, CFP, and RadialFace, a real distorted face benchmark compared with state-of-the-art methods.

This paper presents a novel method based on the generative adversarial network for radial distortion correction. Instead of generating a corrected image, our generator predicts a pixel flow map to measure the pixel offset between the distorted and corrected image. The quality of the generated pixel flow map and the warped image are judged by the discriminator. As texture far away from the image center has strong distortion, we develop an Adaptive Inverted Foveal layer which can transform the deformation to the intensity of the image to exploit this property. Rotation symmetry enhanced convolution kernels are applied to extract geometric features of different orientations explicitly. These learned features are recalibrated using the Squeeze-and-Excitation block to assign different weights for different directions. Moreover, we construct a first real-world radial distorted image dataset RD600 annotated with ground truth to evaluate our proposed method. We conduct extensive experiments to validate the effectiveness of each part of our framework. The further experiment shows our approach outperforms previous methods in both synthetic and real-world datasets quantitatively and qualitatively.

In this paper, we propose a graph-based feature aggregation network (G-FAN) for video face recognition. Compared with the still image, video face recognition exhibits great challenges due to huge intra-class variability and high interclass ambiguity. To address this problem, our G-FAN first uses a Convolutional Neural Network to extract deep features for every input face of a subject. Then, we build an affinity graph based on the relationship between facial features and apply Graph Convolutional Network to generate fine-grained quality vectors for each frame. Finally, the features among multiple frames are adaptively aggregated into a discriminative vector to represent a video face. Different from previous works that take a single image as input, our G-FAN could utilize the correlation information between image pairs and aggregate a template of face images simultaneously. The experiments on video face recognition benchmarks, including YTF, IJB-A, and IJB-C show that: (i) G-FAN automatically learns to advocate high-quality frames while repelling low-quality ones. (ii) G-FAN significantly boosts recognition accuracy and outperforms other state-of-the-art aggregation methods.

3D orientation estimation is a key component of many important computer vision tasks such as autonomous navigation and 3D scene understanding. This paper presents a new CNN architecture to estimate the 3D orientation of an omnidirectional camera with respect to the world coordinate system from a single spherical panorama. To train the proposed architecture, we leverage a dataset of panoramas named VOP60K from Google Street View with labeled 3D orientation, including 50 thousand panoramas for training and 10 thousand panoramas for testing. Previous approaches usually estimate 3D orientation under pinhole cameras. However, for a panorama, due to its larger field of view, previous approaches cannot be suitable. In this paper, we propose an edge extractor layer to utilize the low-level and geometric information of panorama, an attention module to fuse different features generated by previous layers. A regression loss for two column vectors of the rotation matrix and classification loss for the position of vanishing points are added to optimize our network simultaneously. The proposed algorithm is validated on our benchmark, and experimental results clearly demonstrate that it outperforms previous methods.

Inferring 3D geometric cues is a crucial step, whereas vanishing point plays a very important role in image understanding from a single image of structured scenes. In this paper, we construct a 330-thousand-item image database of structured scenes labeled by vanishing points, focal length and camera orientation. We grab over 300 thousand Google Street View images which cover the downtown and neighboring areas of New York, Los Angeles, Chicago and etc. The prediction error is characterized by a loss function by imposing a regularization item derived from the geometric constraint of orthogonal vanishing points and focal length. Moreover, we collect about 30 thousand indoor images using a full 360-degree panorama camera taken by ourselves in room, office, library and etc. We also using Convolutional Neural Networks to transfer learning from street view images to indoor images. Extensive experiments demonstrate that our algorithm outperforms state-of-the-art non-learned approaches.

Radial lens distortion often exists in images taken by commercial cameras, which does not satisfy the assumption of pinhole camera model. Eliminating the radial lens distortion of an image is necessary as a preprocessing step for many vision applications. Some paper has employed Convolutional Neural Networks (CNNs), to achieve radial distortion correction. They generated images with a large number of images of high variation of radial distortion, which can be well exploited by deep CNN with a high learning capacity, and reach the state-of-the-art results. In this paper, we claim that a weight layer with inverted foveal models can be added to these existing CNNs methods for radial distortion correction. In the widely used very deep Resnet-18 model, our method achieves about 20 percent decrease in the loss function with faster convergence compared to the previous methods.