Hand gesture is a new emerging biometric trait containing both physiological and behavioral characteristics. With the popularity of various cameras, and the rich identity features and contactless authentication mode embedded in gestures themselves, vision-based hand gesture authentication has great potential value. However, current hand gesture authentication methods heavily rely on defined gestures and require identical enrollment and verification gestures, which limits the user-friendliness and efficiency of authentication. It is arguably true that authentication in a simpler and faster way, without the need to remember gestures, will be more approachable. Thus, a fast dynamic random hand gesture authentication method is introduced, in which users can perform a random improvised gesture in both the enrollment and verification stage. To better utilize the physiological and behavioral characteristics of hand gestures, an efficient network named Temporal Difference Symbiotic Neural Network (TDS-Net) equipped with our designed behavioral energy-based feature fusion module (BE-Fusion module) is proposed. Extensive experiments on the SCUT-DHGA dataset demonstrate that TDS-Net outperforms the recent state-of-the-art methods.

In recent years, biometrics have received considerable attention for its reliability and usability. Dynamic-hand-gesture is one of the representative biometric modalities, with advantages of safety and template-replaceability, has huge potential value. However, due to the lack of large-scale dataset and comprehensive evaluation methods, few researches are intended to study the dynamic-hand-gesture authentication method. In this article, we introduce a new dataset SCUT-DHGA, which is the first large-scale Dynamic-Hand-Gestures-Authentication dataset. SCUT-DHGA contains 29,160 dynamic-hand-gesture video sequences and more than 1.86 million frames for both color and depth modalities acquired from 193 volunteers. Six kinds of dynamic-hand-gestures are carefully designed for researching two types of authentication tasks: gesture-predefined authentication and gesture-free authentication. To investigate the hypothesis that users' gestures would be variant after time-span, which will degrade the performance of a dynamic-hand-gesture authentication system, two separate sessions' data were acquired from 50 volunteers with an average interval of one week. Beside the SCUT-DHGA dataset, we also benchmark this dataset with our proposed DHGA-net. By releasing such a large-scale dataset and benchmark, we expect dynamic-hand-gesture authentication methods to gain further improvement and generalization.

Object detectors trained on fully-annotated data currently yield state of the art performance but require expensive manual annotations. On the other hand, weakly-supervised detectors have much lower performance and cannot be used reliably in a realistic setting. In this paper, we study the hybrid-supervised object detection problem, aiming to train a high quality detector with only a limited amount of fully-annotated data and fully exploiting cheap data with image-level labels. State of the art methods typically propose an iterative approach, alternating between generating pseudo-labels and updating a detector. This paradigm requires careful manual hyper-parameter tuning for mining good pseudo labels at each round and is quite time-consuming. To address these issues, we present EHSOD, an end-to-end hybrid-supervised object detection system which can be trained in one shot on both fully and weakly-annotated data. Specifically, based on a two-stage detector, we proposed two modules to fully utilize the information from both kinds of labels: 1) CAM-RPN module aims at finding foreground proposals guided by a class activation heat-map; 2) hybrid-supervised cascade module further refines the bounding-box position and classification with the help of an auxiliary head compatible with image-level data. Extensive experiments demonstrate the effectiveness of the proposed method and it achieves comparable results on multiple object detection benchmarks with only 30% fully-annotated data, e.g. 37.5% mAP on COCO. We will release the code and the trained models.

The dominant object detection approaches treat each dataset separately and fit towards a specific domain, which cannot adapt to other domains without extensive retraining. In this paper, we address the problem of designing a universal object detection model that exploits diverse category granularity from multiple domains and predict all kinds of categories in one system. Existing works treat this problem by integrating multiple detection branches upon one shared backbone network. However, this paradigm overlooks the crucial semantic correlations between multiple domains, such as categories hierarchy, visual similarity, and linguistic relationship. To address these drawbacks, we present a novel universal object detector called Universal-RCNN that incorporates graph transfer learning for propagating relevant semantic information across multiple datasets to reach semantic coherency. Specifically, we first generate a global semantic pool by integrating all high-level semantic representation of all the categories. Then an Intra-Domain Reasoning Module learns and propagates the sparse graph representation within one dataset guided by a spatial-aware GCN. Finally, an Inter-Domain Transfer Module is proposed to exploit diverse transfer dependencies across all domains and enhance the regional feature representation by attending and transferring semantic contexts globally. Extensive experiments demonstrate that the proposed method significantly outperforms multiple-branch models and achieves the state-of-the-art results on multiple object detection benchmarks (mAP: 49.1% on COCO).