Collaborative Face Recognition Using a Network of Embedded Cameras

Kulathumani, Vinod; Parupati, Srikanth; Ross, Arun; Jillela, Raghavender

doi:10.1007/978-0-85729-127-1_25

Cited by 8 publications

(15 citation statements)

References 7 publications

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“…Therefore, without loss of generality, in the following we extensively and systematically study the impact 5 For an original image represented with -bit luminance depth, and the corresponding encoded and decoded image , the PSNR is defined as 6 These values have been obtained for an absolute coding cost of view-0 AVC coding of 48 Kb/s, using a minimum QP equal to 32. of the estimated CSA on the observed MVC efficiency, and we assume the encoder settings to be fixed as in Table I. On the Akko&Kayo sequence, we also calculated the lowcomplexity cross-correlation based CSA estimator between the -th frames of reference view 0 and of the -th sequence view, namely .…”

Section: B Experimental Resultsmentioning

confidence: 99%

“…The availability of different views of the same scene enables multi-view oriented processing techniques, such as video scene summarization [3], moving object detection [4], face recognition [5], depth estimation [6], among others. Enhanced application-layer services that rely on these techniques can be envisaged, including multi-person tracking, biometric identification, ambience intelligence, and free-view point video monitoring.…”

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confidence: 99%

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An Empirical Model of Multiview Video Coding Efficiency for Wireless Multimedia Sensor Networks

Colonnese

Cuomo

Melodia

2013

IEEE Trans. Multimedia

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Section: B Experimental Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

An Empirical Model of Multiview Video Coding Efficiency for Wireless Multimedia Sensor Networks

Colonnese

Cuomo

Melodia

2013

IEEE Trans. Multimedia

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“…An example of such a system is the combination of a fixed camera and PTZ camera that is used for closeup tracking of humans and subsequent identification. In our approach instead of continuously tracking an individual at close quarters to eventually get a good view that is suitable for recognition, we rely on redundancy offered by multiple camera views to opportunistically acquire a suitable face image for identification [25].…”

Section: Related Workmentioning

confidence: 99%

Collaborative acquisition of multi-view face images in real-time using a wireless camera network

Parupati

Bakkannagari

Sankar

et al. 2011

2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras

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Abstract-In order to support real-time face recognition using a wireless camera network, we design a data acquisition service to quickly and reliably acquire face images of human subjects from multiple views and to simultaneously index each acquired image into its corresponding pose. In comparison with detection of frontal faces, the detection of non-frontal faces with unknown pose is a much more challenging problem that involves significant image processing. In this paper, we describe a collaborative approach in which multi-view camera geometry and inter-camera communication is utilized at run time to significantly reduce the required processing time. By doing so, we are able to achieve a high capture rate for both frontal and non-frontal faces and at the same time maintain a high detection accuracy. We implement our face acquisition system on a 1.6 GHz Intel Atom Processor based embedded camera network and show that we can reliably acquire frontal faces at 11 fps and non-frontal faces at 10 fps on images captured at a resolution of 640 by 480 pixels.

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“…When different sensors cameras acquire different views of the same scene, multi-view oriented processing techniques enable tasks such as video scene summarization [3], moving object detection [4], face recognition [5], depth estimation for 3D video rendering [6], surveillance, and social robotics to cite a few.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Multiview Video Coding in clustered Multimedia Sensor networks

Colonnese

Cuomo

Melodia

2012

2012 IEEE Global Communications Conference (GLOBECOM)

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Abstract-We experimentally characterize the compression efficiency of Multiview Video Coding (MVC) techniques in Wireless Multimedia Sensor network (WMSN) composed of multiple video cameras with possibly overlapping field of views. We derive an empirical model that predicts the compression efficiency as a function of the common sensed area (CSA) between different camera views. We show that the CSA depends not only on geometrical relationships among the relative positions of different cameras, but also on several object-related phenomena, e.g., occlusions and motion, and on low-level phenomena such as variations in illumination. We then apply the model to a WMSN, where we create clusters based on the CSA as estimated by exchanging local data. Based on this estimates, we form clusters and measure the resulting transmission rate. Numerical simulation results show that building clusters based on a CSA criterion can bring significant performance gains in terms of bandwidth efficiency. The herein presented promising results pave the way for clustering optimization taking into account different networks constraints and conditions.

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Collaborative Face Recognition Using a Network of Embedded Cameras

Cited by 8 publications

References 7 publications

An Empirical Model of Multiview Video Coding Efficiency for Wireless Multimedia Sensor Networks

An Empirical Model of Multiview Video Coding Efficiency for Wireless Multimedia Sensor Networks

Collaborative acquisition of multi-view face images in real-time using a wireless camera network

Leveraging Multiview Video Coding in clustered Multimedia Sensor networks

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