Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication

We investigate whether a classifier can continuously authenticate users based on the way they interact with the touchscreen of a smart phone. We propose a set of 30 behavioral touch features that can be extracted from raw touchscreen logs and demonstrate that different users populate distinct subspaces of this feature space. In a systematic experiment designed to test how this behavioral pattern exhibits consistency over time, we collected touch data from users interacting with a smart phone using basic navigation maneuvers, i.e., up-down and left-right scrolling. We propose a classification framework that learns the touch behavior of a user during an enrollment phase and is able to accept or reject the current user by monitoring interaction with the touch screen. The classifier achieves a median equal error rate of 0% for intra-session authentication, 2%-3% for inter-session authentication and below 4% when the authentication test was carried out one week after the enrollment phase. While our experimental findings disqualify this method as a standalone authentication mechanism for long-term authentication, it could be implemented as a means to extend screen-lock time or as a part of a multi-modal biometric authentication system.Comment: to appear at IEEE Transactions on Information Forensics & Security; Download data from http://www.mariofrank.net/touchalytics

Video interaction using pen-based technology

Dissertação para obtenção do Grau de Doutor em InformáticaVideo can be considered one of the most complete and complex media and its manipulating is still a difficult and tedious task. This research applies pen-based technology to video manipulation, with the goal to improve this interaction. Even though the human familiarity with pen-based devices, how they can be used on video interaction, in order to improve it, making it more natural and at the same time fostering the user’s creativity is an open question. Two types of interaction with video were considered in this work: video annotation and video editing. Each interaction type allows the study of one of the interaction modes of using pen-based technology: indirectly, through digital ink, or directly, trough pen gestures or pressure. This research contributes with two approaches for pen-based video interaction: pen-based video annotations and video as ink. The first uses pen-based annotations combined with motion tracking algorithms, in order to augment video content with sketches or handwritten notes. It aims to study how pen-based technology can be used to annotate a moving objects and how to maintain the association between a pen-based annotations and the annotated moving object The second concept replaces digital ink by video content, studding how pen gestures and pressure can be used on video editing and what kind of changes are needed in the interface, in order to provide a more familiar and creative interaction in this usage context.This work was partially funded by the UTAustin-Portugal, Digital Media, Program (Ph.D. grant: SFRH/BD/42662/2007 - FCT/MCTES); by the HP Technology for Teaching Grant Initiative 2006; by the project "TKB - A Transmedia Knowledge Base for contemporary dance" (PTDC/EAT/AVP/098220/2008 funded by FCT/MCTES); and by CITI/DI/FCT/UNL (PEst-OE/EEI/UI0527/2011

Freeform User Interfaces for Graphical Computing

報告番号: 甲15222 ; 学位授与年月日: 2000-03-29 ; 学位の種別: 課程博士 ; 学位の種類: 博士(工学) ; 学位記番号: 博工第4717号 ; 研究科・専攻: 工学系研究科情報工学専

Multi-Modal Interfaces for Sensemaking of Graph-Connected Datasets

The visualization of hypothesized evolutionary processes is often shown through phylogenetic trees. Given evolutionary data presented in one of several widely accepted formats, software exists to render these data into a tree diagram. However, software packages commonly in use by biologists today often do not provide means to dynamically adjust and customize these diagrams for studying new hypothetical relationships, and for illustration and publication purposes. Even where these options are available, there can be a lack of intuitiveness and ease-of-use. The goal of our research is, thus, to investigate more natural and effective means of sensemaking of the data with different user input modalities. To this end, we experimented with different input modalities, designing and running a series of prototype studies, ultimately focusing our attention on pen-and-touch. Through several iterations of feedback and revision provided with the help of biology experts and students, we developed a pen-and-touch phylogenetic tree browsing and editing application called PhyloPen. This application expands on the capabilities of existing software with visualization techniques such as overview+detail, linked data views, and new interaction and manipulation techniques using pen-and-touch. To determine its impact on phylogenetic tree sensemaking, we conducted a within-subject comparative summative study against the most comparable and commonly used state-of-the-art mouse-based software system, Mesquite. Conducted with biology majors at the University of Central Florida, each used both software systems on a set number of exercise tasks of the same type. Determining effectiveness by several dependent measures, the results show PhyloPen was significantly better in terms of usefulness, satisfaction, ease-of-learning, ease-of-use, and cognitive load and relatively the same in variation of completion time. These results support an interaction paradigm that is superior to classic mouse-based interaction, which could have the potential to be applied to other communities that employ graph-based representations of their problem domains

An Annotation Tool for a Digital Library System of Epidermal Data

Melanoma is one of the deadliest form of skin cancers so it becomes crucial the developing of automated systems that analyze and investigate epidermal images to early identify them also reducing unnecessary medical exams. A key element is the availability of user-friendly annotation tools that can be used by non-IT experts to produce well-annotated and high-quality medical data. In this work, we present an annotation tool to manually crate and annotate digital epidermal images, with the aim to extract meta-data (annotations, contour patterns and intersections, color information) stored and organized in an integrated digital library. This tool is obtained following rigid usability principles also based on doctors interviews and opinions. A preliminary but functional evaluation phase has been conducted with non-medical subjects by using questionnaires, in order to check the general usability and the efficacy of the proposed tool

Phrasing Bimanual Interaction for Visual Design

Architects and other visual thinkers create external representations of their ideas to support early-stage design. They compose visual imagery with sketching to form abstract diagrams as representations. When working with digital media, they apply various visual operations to transform representations, often engaging in complex sequences. This research investigates how to build interactive capabilities to support designers in putting together, that is phrasing, sequences of operations using both hands. In particular, we examine how phrasing interactions with pen and multi-touch input can support modal switching among different visual operations that in many commercial design tools require using menus and tool palettes—techniques originally designed for the mouse, not pen and touch. We develop an interactive bimanual pen+touch diagramming environment and study its use in landscape architecture design studio education. We observe interesting forms of interaction that emerge, and how our bimanual interaction techniques support visual design processes. Based on the needs of architects, we develop LayerFish, a new bimanual technique for layering overlapping content. We conduct a controlled experiment to evaluate its efficacy. We explore the use of wearables to identify which user, and distinguish what hand, is touching to support phrasing together direct-touch interactions on large displays. From design and development of the environment and both field and controlled studies, we derive a set methods, based upon human bimanual specialization theory, for phrasing modal operations through bimanual interactions without menus or tool palettes

Contex-aware gestures for mixed-initiative text editings UIs

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Interacting with computers following peer review. The version of record is available online at: http://dx.doi.org/10.1093/iwc/iwu019[EN] This work is focused on enhancing highly interactive text-editing applications with gestures. Concretely, we study Computer Assisted Transcription of Text Images (CATTI), a handwriting transcription system that follows a corrective feedback paradigm, where both the user and the system collaborate efficiently to produce a high-quality text transcription. CATTI-like applications demand fast and accurate gesture recognition, for which we observed that current gesture recognizers are not adequate enough. In response to this need we developed MinGestures, a parametric context-aware gesture recognizer. Our contributions include a number of stroke features for disambiguating copy-mark gestures from handwritten text, plus the integration of these gestures in a CATTI application. It becomes finally possible to create highly interactive stroke-based text-editing interfaces, without worrying to verify the user intent on-screen. We performed a formal evaluation with 22 e-pen users and 32 mouse users using a gesture vocabulary of 10 symbols. MinGestures achieved an outstanding accuracy (<1% error rate) with very high performance (<1 ms of recognition time). We then integrated MinGestures in a CATTI prototype and tested the performance of the interactive handwriting system when it is driven by gestures. Our results show that using gestures in interactive handwriting applications is both advantageous and convenient when gestures are simple but context-aware. Taken together, this work suggests that text-editing interfaces not only can be easily augmented with simple gestures, but also may substantially improve user productivity.This work has been supported by the European Commission through the 7th Framework Program (tranScriptorium: FP7- ICT-2011-9, project 600707 and CasMaCat: FP7-ICT-2011-7, project 287576). It has also been supported by the Spanish MINECO under grant TIN2012-37475-C02-01 (STraDa), and the Generalitat Valenciana under grant ISIC/2012/004 (AMIIS).Leiva, LA.; Alabau, V.; Romero Gómez, V.; Toselli, AH.; Vidal, E. (2015). Contex-aware gestures for mixed-initiative text editings UIs. Interacting with Computers. 27(6):675-696. https://doi.org/10.1093/iwc/iwu019S675696276Alabau V. Leiva L. A. Transcribing Handwritten Text Images with a Word Soup Game. Proc. Extended Abstr. Hum. Factors Comput. Syst. (CHI EA) 2012.Alabau V. Rodríguez-Ruiz L. Sanchis A. Martínez-Gómez P. Casacuberta F. 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Integrating Multiple Sketch Recognition Methods to Improve Accuracy and Speed

Sketch recognition is the computer understanding of hand drawn diagrams. Recognizing sketches instantaneously is necessary to build beautiful interfaces with real time feedback. There are various techniques to quickly recognize sketches into ten or twenty classes. However for much larger datasets of sketches from a large number of classes, these existing techniques can take an extended period of time to accurately classify an incoming sketch and require significant computational overhead. Thus, to make classification of large datasets feasible, we propose using multiple stages of recognition. In the initial stage, gesture-based feature values are calculated and the trained model is used to classify the incoming sketch. Sketches with an accuracy less than a threshold value, go through a second stage of geometric recognition techniques. In the second geometric stage, the sketch is segmented, and sent to shape-specific recognizers. The sketches are matched against predefined shape descriptions, and confidence values are calculated. The system outputs a list of classes that the sketch could be classified as, along with the accuracy, and precision for each sketch. This process both significantly reduces the time taken to classify such huge datasets of sketches, and increases both the accuracy and precision of the recognition

Integrating Multiple Sketch Recognition Methods to Improve Accuracy and Speed

Sketch recognition is the computer understanding of hand drawn diagrams. Recognizing sketches instantaneously is necessary to build beautiful interfaces with real time feedback. There are various techniques to quickly recognize sketches into ten or twenty classes. However for much larger datasets of sketches from a large number of classes, these existing techniques can take an extended period of time to accurately classify an incoming sketch and require significant computational overhead. Thus, to make classification of large datasets feasible, we propose using multiple stages of recognition. In the initial stage, gesture-based feature values are calculated and the trained model is used to classify the incoming sketch. Sketches with an accuracy less than a threshold value, go through a second stage of geometric recognition techniques. In the second geometric stage, the sketch is segmented, and sent to shape-specific recognizers. The sketches are matched against predefined shape descriptions, and confidence values are calculated. The system outputs a list of classes that the sketch could be classified as, along with the accuracy, and precision for each sketch. This process both significantly reduces the time taken to classify such huge datasets of sketches, and increases both the accuracy and precision of the recognition