Both unmanned vehicles and driver assistance systems require solving the problem of traffic sign recognition. A lot of work has been done in this area, but no approach has been presented to perform the task with high accuracy and high speed under various conditions until now. In this paper, we have designed and implemented a detector by adopting the framework of faster R-convolutional neural networks (CNN) and the structure of MobileNet. Here, color and shape information have been used to refine the localizations of small traffic signs, which are not easy to regress precisely. Finally, an efficient CNN with asymmetric kernels is used to be the classifier of traffic signs. Both the detector and the classifier have been trained on challenging public benchmarks. The results show that the proposed detector can detect all categories of traffic signs. The detector and the classifier proposed here are proved to be superior to the state-of-the-art method. Our code and results are available online.

Accurate facial expression recognition is challenging because identity biases introduce large intraclass variations and high interclass similarities. Most existing facial expression recognition approaches are devoted to alleviate the effects of identity. However, based on the theories of cognitive science, psychology, and physiology, this article argues that the identity information is important and can promote expression recognition. Motivated by our investigation of the influences of identity on facial expression recognition, this article proposes an identity–expression dual branch network (IE-DBN) for facial expression recognition. First, identity-related features and expression-related features are learnt from the same input facial expression image by two branches respectively. Then, those two features are aggregated with our bilinear module. The bilinear aggregation not only emphasizes the impact of identity but also improves interclass variations and intraclass similarities. The joint training strategy is proposed to regularize the identity-related features. It can force our network to generate expression-guided identity-related features and suppresses negative identity factors in the same time. Experiments on three popular facial expression databases, including two popular posed facial expression databases and one spontaneous facial expression database, show that our IE-DBN outperforms most of the state of the arts, which demonstrates our superiority in facial expression recognition.

Reconstructing a full-resolution stereoscopic video from an asymmetric stereoscopic video is a challenging task. The existing approaches require depth information, which imposes an additional challenge in data acquisition. In this paper, we propose a novel scheme that is capable of obtaining super-resolution and depth estimation simultaneously from an asymmetric stereoscopic video. The proposed scheme models the video super-resolution and stereo matching with a unified energy function. Then, we apply an alternating optimization method to minimize this energy function, which can be implemented with a two-step algorithm. In the first step we calculate the initial depth map by using a region-based cooperative optimization technique while considering the temporal consistency in video. In the second step we resolve the super-resolution problem under the guidance of the depth information. It is effective because each step benefits from the additional improvement over the previous step. We iteratively update the two steps until stable depth and super-resolution results are obtained. We have conducted a series of experiments on public stereoscopic video sequences to evaluate the performance of the proposed method. Both objective indexes and subjective visual comparisons verify that the proposed scheme can achieve satisfactory super-resolution results and high-quality depth map simultaneously. In particular, the subjective evaluation experiments on a 3-D monitor show that this scheme outperforms others and achieves the best visual sharpness.