Umdaa 02 Dataset, 2015 to 20 Dec. This database comprises of smart
- Umdaa 02 Dataset, 2015 to 20 Dec. This database comprises of smartphone sensor signals acquired during natural human-mobile interaction. The experiments are conducted on the UMDAA-02 mobile database [6], a challenging dataset acquired under natural conditions. io In this paper, automated user verification techniques for smartphones are investigated. The UMDAA-02-FD dataset includes number of front-facing camera images. umdaa02. Each user had performed five different tasks in the data acquisition phase, such as enrollment, document, picture, popup, and scrolling tasks. from publication: Adversarial domain adaptive subspace clustering 1) UMDAA-02 Application-Usage Dataset: The UMDAA- 02 dataset is specifically designed for evaluating active au- thentication systems in the wild. The proposed One Class CNN (OC-CNN) is evaluated on the UMDAA-02 Face, Abnormality-1001, FounderType-200 datasets. The majority of state-of-the-art solutions in this domain are based on “device unlock” scenario—checking of information (authentication factors) provided by the user for unlocking a smartphone. 2 makes an overview of the state-of-the-art works related and links with this work. This paper focuses on three sensors - front camera, touch sensor and location service while providing a general description for other UMDAA-02 Application-Usage Dataset: The UMDAA- dataset is specifically designed for evaluating active au-thentication systems in the wild. Previous works have demonstrated the potential of biometric and behavioral-based profiling patterns for user authentication under controlled scenarios. This paper focuses on three sensors - front camera, touch sensor and location service while providing a general description for other modalities. A unique non-commercial dataset, the University of Maryland Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentica-tion research is introduced. width=100%> <HR> <h2>Overview</h2> The UMDAA-02 data set consists of 141. This study uses a fusion of two popular publicly available datasets the Hand Movement Orientation and Grasp dataset and the BioIdent dataset. 马里兰大学主动认证数据集（UMD Active Authentication Dataset - 02，简称UMDAA02）是一个广泛用于生物识别研究和身份验证系统的数据集。 这个数据集由马里兰大学的研究团队创建，旨在支持对用户行为进行无感知、持续的认证方法的研究。 University of Maryland Active Authentication Dataset - 02 - umdaa02/umdaa02. Experiments show that the proposed method is able perform better than the prev ous one-class classification-based methods an Our experiments are conducted on the semi-uncontrolled UMDAA-02 database. The results are analyzed using the UMDAA-02 Face Dataset (UMDAA-02-FD). The dataset consists of 141. io Public University of Maryland Active Authentication Dataset - 02 HTML 1 University of Maryland Active Authentication Dataset 02 (UMDAA-02) (Mahbub et al. github. This paper focuses on three sensors - front camera, touch sensor and location service while providing a general description for other modalities In this paper, automated user verification techniques for smartphones are investigated. These datasets are related to a variety of one class application problems such as user authentication, abnormality detection and novelty detection. Each column represents sample images obtained for the same user. To validate proposed systems experiments have been conducted on face images of varying resolutions ranging from very low to low (10 × 10, 20 × 20, 30 × 30, and 40 × 40) of the UMDAA-02 dataset. The first three rows show correct detections at different illumination The experiments are conducted on the UMDAA-02 mobile database [6], a challenging dataset acquired under natural conditions. Our datasets are collected when users are filling out a registration form in a seated posture, which is a common activity. from publication A unique non-commercial dataset, the University of Maryland Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentication research is introduced. . Note that the The proposed one-class CNN is evaluated on the UMDAA-02 Face, Abnormality-1001, and FounderType-200 datasets. Another part-based method for detecting partial and occluded faces on mobile devices was developed in [44]. This process is illustrated in Fig. The dataset was collected from 48 subjects using Download scientific diagram | Sample images from the UMDAA-01 dataset [6]. (b)MOBIO. This paper focuses on three sensors - front camera, touch sensor and location service while providing a general description for other modalities For this, our experiments are conducted on the UMDAA-02 mobile database [6], a challenging mobile dataset acquired under unsupervised conditions.