Gait-based person authentication by wearable cameras

Kohei, Shiraga; Trung, Ngo Thanh; Mitsugami, Ikuhisa; Mukaigawa, Yasuhiro; Yagi, Yasushi

doi:10.1109/inss.2012.6240524

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…a private office). We remark that this issue has been addressed also by Shiraga et al in [37]. The authors deployed two cameras on a back-pack to capture what happens behind the wearer when walking.…”

Section: Threat Modelmentioning

confidence: 90%

Personalized Image-based User Authentication using Wearable Cameras

Nguyen¹,

Sigg²

2016

Preprint

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Personal devices (e.g. laptops, tablets, and mobile phones) are conventional in daily life and have the ability to store users' private data. The security problems related to these appliances have become a primary concern for both users and researchers. In this paper, we analyse first-person-view videos to develop a personalized user authentication mechanism. Our proposed algorithm generates provisional image-based passwords which benefit a variety of purposes such as unlocking a mobile device or fallback authentication. First, representative frames are extracted from the egocentric videos. Then, they are split into distinguishable segments before a clustering procedure is applied to discard repetitive scenes. The whole process aims to retain memorable images to form the authentication challenges. We integrate eye tracking data to select informative sequences of video frames and suggest a blurriness-based method if an eye-facing camera is not available. To evaluate our system, we perform experiments in different settings including objectinteraction activities and traveling contexts. Even though our mechanism produces variable graphical passwords, the log-in effort for the user is comparable with approaches based on static challenges. We verified the authentication challenges in the presence of a random and an informed attacker who is familiar with the environment and observed that the time required and the number of attempts are significantly higher than for the legitimate user, making it possible to detect attacks on the authentication system.1 Transcend DrivePro TM Body 10 wearable camera: https://www.transcendinfo.com/Products/No-704

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Section: Threat Modelmentioning

confidence: 90%

Personalized Image-based User Authentication using Wearable Cameras

Nguyen¹,

Sigg²

2016

Preprint

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“…The identity of the camera wearer from the single egocentric perspective is challenging to discover due to the limited field of view often obscuring the wearer's body. Shiraga et al (2012) attempted to recover this information using a complex system of stereo cameras on the user's backpack to analyse motion during walking. On the other hand, Hoshen and Peleg (2016) proposed an approach based solely on a front camera and the head motion signature present in the egocentric video.…”

Section: Person Identificationmentioning

confidence: 99%

An Outlook into the Future of Egocentric Vision

Plizzari,

Goletto,

Furnari

et al. 2024

Int J Comput Vis

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What will the future be? We wonder! In this survey, we explore the gap between current research in egocentric vision and the ever-anticipated future, where wearable computing, with outward facing cameras and digital overlays, is expected to be integrated in our every day lives. To understand this gap, the article starts by envisaging the future through character-based stories, showcasing through examples the limitations of current technology. We then provide a mapping between this future and previously defined research tasks. For each task, we survey its seminal works, current state-of-the-art methodologies and available datasets, then reflect on shortcomings that limit its applicability to future research. Note that this survey focuses on software models for egocentric vision, independent of any specific hardware. The paper concludes with recommendations for areas of immediate explorations so as to unlock our path to the future always-on, personalised and life-enhancing egocentric vision.

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“…Recognition from accelerometers carried on the user's body [16] is also reported. Shiraga et al [23] studied people recognition wearing a backpack with stereo cameras. Rotation and period of motion were computed using 3D geometry, and users were accurately recognized.…”

Section: Previous Workmentioning

confidence: 99%

An Egocentric Look at Video Photographer Identity

Hoshen

Peleg

2016

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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Egocentric cameras are being worn by an increasing number of users, among them many security forces worldwide. GoPro cameras already penetrated the mass market, reporting substantial increase in sales every year. As headworn cameras do not capture the photographer, it may seem that the anonymity of the photographer is preserved even when the video is publicly distributed.We show that camera motion, as can be computed from the egocentric video, provides unique identity information. The photographer can be reliably recognized from a few seconds of video captured when walking. The proposed method achieves more than 90% recognition accuracy in cases where the random success rate is only 3%.Applications can include theft prevention by locking the camera when not worn by its rightful owner. Searching video sharing services (e.g. YouTube) for egocentric videos shot by a specific photographer may also become possible.

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Gait-based person authentication by wearable cameras

Cited by 6 publications

References 12 publications

Personalized Image-based User Authentication using Wearable Cameras

Personalized Image-based User Authentication using Wearable Cameras

An Outlook into the Future of Egocentric Vision

An Egocentric Look at Video Photographer Identity

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