Investigating Biometric Response for Information Retrieval Applications

Mooney, Colum; Scully, M F; Jones, Gareth J. F.; Smeaton, Alan F.

doi:10.1007/11735106_67

Cited by 16 publications

(12 citation statements)

References 5 publications

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“…For example, Mooney et al [11] performed a preliminary study of the role of viewer's physiological states in an attempt to improve data indexing for search and within the search process itself. Participants' physiological responses to emotional stimuli were recorded using a range of biometric measurements, such as galvanic skin response (GSR), skin temperature, and other.…”

Section: Affective Video Summarisationmentioning

confidence: 99%

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Exploiting facial expressions for affective video summarisation

Joho

Jose

Valenti

et al. 2009

Proceedings of the ACM International Conference on Image and Video Retrieval

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This paper presents an approach to affective video summarisation based on the facial expressions (FX) of viewers. A facial expression recognition system was deployed to capture a viewer's face and his/her expressions. The user's facial expressions were analysed to infer personalised affective scenes from videos. We proposed two models, pronounced level and expression's change rate, to generate affective summaries using the FX data. Our result suggested that FX can be a promising source to exploit for affective video summaries that can be tailored to individual preferences.

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Section: Affective Video Summarisationmentioning

confidence: 99%

“…However, this area has not been fully exploited, and existing techniques to generate perception based summaries are expensive. For example, they requires manual annotations [17] or several physiological sensors [9,11] to capture people's affective state.…”

Section: Introductionmentioning

confidence: 99%

Exploiting facial expressions for affective video summarisation

Joho

Jose

Valenti

et al. 2009

Proceedings of the ACM International Conference on Image and Video Retrieval

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“…The former is related to the website's perceived usefulness in solving the task at hand, while the latter to the general interest in its content and its aesthetic features. In [19], Mooney et al performed a preliminary study of the role of physiological states, in an attempt to improve data indexing for search and within the search process itself. Users' physiological responses to emotional stimuli were recorded using a range of biometric measurements (GSR, skin temperature, etc.).…”

Section: Introductionmentioning

confidence: 99%

“…The current work, following the example set by [19], investigates the role of emotions in the information seeking process and the potential impact of task difficulty on users' emotional behaviour. Most importantly, it introduces a new approach to the detection and quantification of affective information, which can be potentially applied in future studies to analyze search behaviour at relevance assessment level.…”

Section: Introductionmentioning

confidence: 99%

Affective feedback

Arapakis

Jose

Gray

2008

Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

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User feedback is considered to be a critical element in the information seeking process, especially in relation to relevance assessment. Current feedback techniques determine content relevance with respect to the cognitive and situational levels of interaction that occurs between the user and the retrieval system. However, apart from real-life problems and information objects, users interact with intentions, motivations and feelings, which can be seen as critical aspects of cognition and decision-making. The study presented in this paper serves as a starting point to the exploration of the role of emotions in the information seeking process. Results show that the latter not only interweave with different physiological, psychological and cognitive processes, but also form distinctive patterns, according to specific task, and according to specific user.

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“…Within a lifelog biometric recordings can be bound to other sources of data (including the events within visual lifelog collections) to provide emotional and affective context for the data [14]. Times of increased emotional intensity may indicate good instances at which to select an emotionally significant or "affective" keyframe.…”

Section: Future Workmentioning

confidence: 99%

Investigating keyframe selection methods in the novel domain of passively captured visual lifelogs

Doherty

Byrne

Smeaton

et al. 2008

Proceedings of the 2008 International Conference on Content-Based Image and Video Retrieval

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The SenseCam is a passive capture wearable camera, worn around the neck, and when worn continuously it takes an average of 1,900 images per day. It can be used to create a personal lifelog or visual recording of the wearer's life which can be helpful as an aid to human memory. For such a large amount of visual information to be useful, it needs to be structured into "events", which can be achieved through automatic segmentation. An important component of this structuring process is the selection of keyframes to represent individual events. This work investigates a variety of techniques for the selection of a single representative keyframe image from each event, in order to provide the user with an instant visual summary of that event. In our experiments we use a large test set of 2,232 lifelog events collected by 5 users over a time period of one month each. We propose a novel keyframe selection technique which seeks to select the image with the highest "quality" as the keyframe. The inclusion of "quality" approaches in keyframe selection is demonstrated to be useful owing to the high variability in image visual quality within passively captured image collections.

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Investigating Biometric Response for Information Retrieval Applications

Cited by 16 publications

References 5 publications

Exploiting facial expressions for affective video summarisation

Exploiting facial expressions for affective video summarisation

Affective feedback

Investigating keyframe selection methods in the novel domain of passively captured visual lifelogs

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