Evaluation of face recognition techniques for application to facebook

Becker, Brian C.; Ortiz, Enrique

doi:10.1109/afgr.2008.4813471

Cited by 48 publications

(29 citation statements)

References 15 publications

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“…We remark that recognition accuracy rate of 0.9 is deemed as adequate for real-world face recognition applications [BO08,GKS10] and, following (5.1), our results can be derived for arbitrary recognition accuracy rates.…”

Section: Face Recognition On the Cloudmentioning

confidence: 56%

“…In Chapter 2, we present the detailed system description and the system model . Each video frame can be cropped to the object or face area by automated face detection algorithms [BO08], such as the well-known Viola-Jones classifier for face detection [JV03] in video frames. Alternatively, the user can be asked to position the mobile device such that its frontal camera places the object or face within a rectangle displayed on the device (smartphone or portable computer) screen prior to the initiation of the video capture.…”

Section: Paper Contributionmentioning

confidence: 99%

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Bandit framework for systematic learning in wireless video-based face recognition

Atan

Tekin

Schaar

et al. 2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Professor Mihaela van der Schaar, Chair Video-based object or face recognition services on mobile devices have recently garnered significant attention, given that video cameras are now ubiquitous in all mobile communication devices. In one of the most typical scenarios for such services, each mobile device captures and transmits video frames over wireless to a remote computing cluster (a.k.a. "cloud" computing infrastructure) that performs the heavy-duty video feature extraction and recognition tasks for a large number of mobile devices. A major challenge of such scenarios stems from the highly-varying contention levels in the wireless transmission, as well as the variation in the task-scheduling congestion in the cloud. In order for each device to adapt the transmission, feature extraction and search parameters and maximize its object or face recognition rate under such contention and congestion variability, we propose a systematic learning framework based on multi-user multi-armed bandits. The performance loss under two instantiations of the proposed framework is characterized by the derivation of upper bounds for the achievable short-term and long-term loss in the expected recognition rate per face recognition attempt against the "oracle" solution that assumes a-priori knowledge of the system performance under every possible setting. Unlike well-known reinforcement learning ii techniques, such as Q-learning, that exhibit very slow convergence when operating in highly-dynamic environments, the proposed bandit-based systematic learning quickly approaches the optimal transmission and cloud resource allocation policies based on feedback on the experienced dynamics (contention and congestion levels).To validate our approach, time-constrained simulation results are presented via: (i) contention-based H.264/AVC video streaming over IEEE 802.11 WLANs and (ii) principal-component based face recognition algorithms running under varying congestion levels of a cloud-computing infrastructure. Against state-ofthe-art reinforcement learning methods, and for the same recognition accuracy rate set for each face recognition transaction between a mobile and the cloud, our bandit-based framework is shown to provide 17.8% ∼ 44.5% reduction of the number of video frames that must be processed by the cloud for recognition and 11.5% ∼ 36.5% reduction in the video traffic over the WLAN.iii The thesis of Onur Atan is approved.

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Section: Face Recognition On the Cloudmentioning

confidence: 56%

Section: Paper Contributionmentioning

confidence: 99%

Bandit framework for systematic learning in wireless video-based face recognition

Atan

Tekin

Schaar

et al. 2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

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“…Recent evaluation of face recognition techniques with the real photo images in Facebook [3] showed that the best performing algorithms can achieve about 65% accuracy using 60,000 facial images of 500 users. This shows that the gap between the legitimate user and a mechanised attack may not be as large as one might think.…”

Section: Automatic Face Recognitionmentioning

confidence: 99%

“…Since we use campus or region networks, the number of communities might be small compared to real friendship patterns in Facebook, which could include structures of high school friends, college classmates, work colleges and so on. Recently, some social networking services such as Google+ 3 and Facebook have started to encourage users to divide their friends into explicit community groups; community-based challenge selection should be even more useful in such situations.…”

Section: Community-based Friend Selectionmentioning

confidence: 99%

Social Authentication: Harder Than It Looks

Kim

Tang

Anderson

2012

Financial Cryptography and Data Security

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Abstract. A number of web service firms have started to authenticate users via their social knowledge, such as whether they can identify friends from photos. We investigate attacks on such schemes. First, attackers often know a lot about their targets; most people seek to keep sensitive information private from others in their social circle. Against close enemies, social authentication is much less effective. We formally quantify the potential risk of these threats. Second, when photos are used, there is a growing vulnerability to face-recognition algorithms, which are improving all the time. Network analysis can identify hard challenge questions, or tell a social network operator which users could safely use social authentication; but it could make a big difference if photos weren't shared with friends of friends by default. This poses a dilemma for operators: will they tighten their privacy default settings, or will the improvement in security cost too much revenue?

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“…and processing constraints at the remote cloud-computing servers where the data analysis takes place. Examples of early commercial services in this domain include Google Goggles, Google Glass, Facebook automatic face tagging [3] and Microsoft's Photo Gallery face recognition. Figure 1 presents an example of such deployments.…”

Section: Introductionmentioning

confidence: 99%

Bandit Framework for Systematic Learning in Wireless Video-Based Face Recognition

Atan¹,

Andreopoulos

Tekin³

et al. 2015

IEEE J. Sel. Top. Signal Process.

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In most video-based object or face recognition services on mobile devices, each device captures and transmits video frames over wireless to a remote computing service (a.k.a. "cloud") that performs the heavy-duty video feature extraction and recognition tasks for a large number of mobile devices. The major challenges of such scenarios stem from the highly-varying contention levels in the wireless local area network (WLAN), as well as the variation in the taskscheduling congestion in the cloud. In order for each device to maximize its object or face recognition rate under such contention and congestion variability, we propose a systematic learning framework based on multi-armed bandits. Unlike well-known reinforcement learning techniques that exhibit very slow convergence rates when operating in highly-dynamic environments, the proposed bandit-based systematic learning quickly approaches the optimal transmission and processing-complexity policies based on feedback on the experienced dynamics (contention and congestion levels). Comparisons against state-of-the-art reinforcement learning methods demonstrate that this makes our proposal especially suitable for the highly-dynamic levels of wireless contention and cloud scheduling congestion.

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Evaluation of face recognition techniques for application to facebook

Cited by 48 publications

References 15 publications

Bandit framework for systematic learning in wireless video-based face recognition

Bandit framework for systematic learning in wireless video-based face recognition

Social Authentication: Harder Than It Looks

Bandit Framework for Systematic Learning in Wireless Video-Based Face Recognition

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