Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future

Cost-Driven Hardware-Software Co-Optimization of Machine Learning Pipelines

Researchers have long touted a vision of the future enabled by a proliferation of internet-of-things devices, including smart sensors, homes, and cities. Increasingly, embedding intelligence in such devices involves the use of deep neural networks. However, their storage and processing requirements make them prohibitive for cheap, off-the-shelf platforms. Overcoming those requirements is necessary for enabling widely-applicable smart devices. While many ways of making models smaller and more efficient have been developed, there is a lack of understanding of which ones are best suited for particular scenarios. More importantly for edge platforms, those choices cannot be analyzed in isolation from cost and user experience. In this work, we holistically explore how quantization, model scaling, and multi-modality interact with system components such as memory, sensors, and processors. We perform this hardware/software co-design from the cost, latency, and user-experience perspective, and develop a set of guidelines for optimal system design and model deployment for the most cost-constrained platforms. We demonstrate our approach using an end-to-end, on-device, biometric user authentication system using a $20 ESP-EYE board

Modality switching and performance in a thought and speech controlled computer game

Providing multiple modalities to users is known to improve the overall performance of an interface. Weakness of one modality can be overcome by the strength of another one. Moreover, with respect to their abilities, users can choose between the modalities to use the one that is the best for them. In this paper we explored whether this holds for direct control of a computer game which can be played using a brain-computer interface (BCI) and an automatic speech recogniser (ASR). Participants played the games in unimodal mode (i.e. ASR-only and BCI-only) and multimodal mode where they could switch between the two modalities. The majority of the participants switched modality during the multimodal game but for the most of the time they stayed in ASR control. Therefore multimodality did not provide a significant performance improvement over unimodal control in our particular setup. We also investigated the factors which influence modality switching. We found that performance and peformance-related factors were prominently effective in modality switching

The Feasibility of Neuroimaging Methods in Marketing Research

On July 17, 1990, President George Bush issued “Proclamation #6158” which boldly declared the following ten years would be called the “Decade of the Brain” (Bush, 1990). Accordingly, the research mandates of all US federal biomedical institutions worldwide were redirected towards the study of the brain in general and cognitive neuroscience specifically. In 2008, one of the greatest legacies of this “Decade of the Brain” is the impressive array of techniques that can be used to study cortical activity. We now stand at a juncture where cognitive function can be mapped in the time, space and frequency domains, as and when such activity occurs. These advanced techniques have led to discoveries in many fields of research and clinical science, including psychology and psychiatry. Unfortunately, neuroscientific techniques have yet to be enthusiastically adopted by the social sciences. Market researchers, as specialized social scientists, have an unparalleled opportunity to adopt cognitive neuroscientific techniques and significantly redefine the field and possibly even cause substantial dislocations in business models. Following from this is a significant opportunity for more commercially-oriented researchers to employ such techniques in their own offerings. This report examines the feasibility of these techniques

Bacteria Hunt: A multimodal, multiparadigm BCI game

Brain-Computer Interfaces (BCIs) allow users to control applications by brain activity. Among their possible applications for non-disabled people, games are promising candidates. BCIs can enrich game play by the mental and affective state information they contain. During the eNTERFACE’09 workshop we developed the Bacteria Hunt game which can be played by keyboard and BCI, using SSVEP and relative alpha power. We conducted experiments in order to investigate what difference positive vs. negative neurofeedback would have on subjects’ relaxation states and how well the different BCI paradigms can be used together. We observed no significant difference in mean alpha band power, thus relaxation, and in user experience between the games applying positive and negative feedback. We also found that alpha power before SSVEP stimulation was significantly higher than alpha power during SSVEP stimulation indicating that there is some interference between the two BCI paradigms

The MAIN Model: A Heuristic Approach to Understanding Technology Effects on Credibility

Part of the Volume on Digital Media, Youth, and Credibility Historically, credibility assessments assume a relatively explicit, effortful evaluation of message source and content, but this chapter argues that four technological features -- modality, agency, interactivity, and navigability -- can profoundly influence credibility judgments that are made more subtly and automatically while accessing information. Based on research evidence that suggests today's youth pay more attention to these technological aspects than to source and content aspects, this chapter examines the ways in which they may shape credibility perceptions during digital media use. These features are conceptualized as "affordances" (or action possibilities) that suggest certain functions and/or transmit certain cues that trigger cognitive heuristics (or mental shortcuts) leading people to their impressions of the quality and credibility of the underlying information