Fast Image Mosaicing for Panoramic Face Recognition

Yang, Fan; Paindavoine, Michel; Abdi, Hervé; Arnoult, Dominique

doi:10.4304/jmm.1.2.14-20

Cited by 11 publications

(8 citation statements)

References 14 publications

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“…As a next step, we would like to integrate our multi-view face acquisition framework with multi-view face recognition techniques and quantify the achievable recognition performance [2], [3], [4], [5]. In particular, we would like to integrate our system with sparse encoding based recognition techniques [27] that are especially well-suited for distributed light-weight implementations.…”

Section: Discussionmentioning

confidence: 99%

“…Often profile (side view) face images contain tattoos, moles and special markers that are useful in human identification [1]. Recent studies have shown that profile views and partial profile views can be used for reliable face recognition with high accuracy [2], [3], [4], [5]. That being said, acquiring multi-view face images is a challenging task in terms of computational overhead especially due to their diversity [6].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…The POI matching process is as follows: the markers are respectively detected in both images by using a color threshold method [30]. It performs a color segmentation based on the hue and the saturation components of each image.…”

Section: Detection and Matching Of Poismentioning

confidence: 99%

“…It is used in various domains such as medicine [32], security [30], virtual reality or robotics Multimed Tools Appl (2012) 57:565-586 …”

Section: Introductionmentioning

confidence: 99%

Evolutionary-based 3D reconstruction using an uncalibrated stereovision system: application of building a panoramic object view

Koch

Dipandà

Bourgeois-Republique

2010

Multimed Tools Appl

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In this paper, we propose an original evolutionary-based method for 3D panoramic reconstruction from an uncalibrated stereovision system (USS). The USS is composed of five cameras located on an arc of a circle around the object to be analyzed. The main originality of this work concerns the process of the calculation of the 3D information. Actually, with our method, 3D coordinates are directly obtained without any prior estimation of the fundamental matrix. The method operates in two steps. Firstly, points of interest are detected in pairs of images acquired by two consecutive cameras of the USS are matched. And secondly, using evolutionary algorithms, we jointly compute the transformed matrix between the two images and the respective depth of the points of interest. The accuracy of the proposed method is validated through a comparison with the depth values obtained using a traditional method. In order to perform 3D panoramic object reconstruction, the process is repeated for all the pairs of consecutive cameras. The 3D points thus obtained throughout the successive steps of the process which correspond to the different points of interest, are combined in order to obtain a set of 3D points all around the analyzed object.Keywords Panoramic 3D reconstruction . Evolutionary algorithm . Uncalibrated system IntroductionPanoramic 3D reconstruction is a recent area of research which began about 10 years ago. It is used in various domains such as medicine [32], security [30], virtual reality or robotics Multimed Tools Appl (2012) 57:565-586

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“…Images of different quality (pose, illumination and resolution) continually stream in from multiple cameras which need to be efficiently utilized. [2,3,4,5]. That being said, acquiring multi-view face images is a challenging task in terms of computational overhead especially due to their diversity [6].…”

Section: Notationmentioning

confidence: 99%

Fast, collaborative acquisition of multi-view face images using a camera network and its impact on real-time human identification

Bakkannagari¹

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Fast Image Mosaicing for Panoramic Face Recognition

Cited by 11 publications

References 14 publications

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

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

Evolutionary-based 3D reconstruction using an uncalibrated stereovision system: application of building a panoramic object view

Fast, collaborative acquisition of multi-view face images using a camera network and its impact on real-time human identification

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