Remote identification of faces: Problems, prospects, and progress

Chellappa, Rama; Ni, Jie; Patel, Vishal M.

doi:10.1016/j.patrec.2011.11.020

Cited by 32 publications

(36 citation statements)

References 41 publications

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“…On the other hand, unsupervised DA is more representative of real-world scenarios. For instance, face recognition systems trained under constrained laboratory environments will encounter great challenges when applied to faces 'in the wild', where the acquired face images suffer from a variety of degradations such as low resolution, poor illumination, blur, pose variation, occlusion etc [8]. Sometimes the coupling effects among these different factors give rise to more variations.…”

Section: Introductionmentioning

confidence: 99%

Subspace Interpolation via Dictionary Learning for Unsupervised Domain Adaptation

Qiu

Chellappa

2013

2013 IEEE Conference on Computer Vision and Pattern Recognition

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176

146

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Domain adaptation addresses the problem where data instances of a source domain have different distributions from that of a target domain, which occurs frequently in many real life scenarios. This work focuses on unsupervised domain adaptation, where labeled data are only available in the source domain. We propose to interpolate subspaces through dictionary learning to link the source and target domains. These subspaces are able to capture the intrinsic domain shift and form a shared feature representation for cross domain recognition. Further, we introduce a quantitative measure to characterize the shift between two domains, which enables us to select the optimal domain to adapt to the given multiple source domains. We present experiments on face recognition across pose, illumination and blur variations, cross dataset object recognition, and report improved performance over the state of the art.

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Section: Introductionmentioning

confidence: 99%

Subspace Interpolation via Dictionary Learning for Unsupervised Domain Adaptation

Qiu

Chellappa

2013

2013 IEEE Conference on Computer Vision and Pattern Recognition

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176

146

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“…To illustrate the effectiveness of our method, we present experimental results on three datasets for video-based face recognition: the UMD dataset [25], the Multiple Biomertic Grand Challenge (MBGC) dataset [26], [27], and the Honda/UCSD dataset [5].…”

Section: Resultsmentioning

confidence: 99%

Video-based face recognition via joint sparse representation

Chen

Patel

Shekhar

et al. 2013

2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)

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Abstract-In video-based face recognition, a key challenge is in exploiting the extra information available in a video; e.g., face, body, and motion identity cues. In addition, different video sequences of the same subject may contain variations in resolution, illumination, pose, and facial expressions. These variations contribute to the challenges in designing an effective video-based face-recognition algorithm. We propose a novel multivariate sparse representation method for video-to-video face recognition. Our method simultaneously takes into account correlations as well as coupling information among the video frames. Our method jointly represents all the video data by a sparse linear combination of training data. In addition, we modify our model so that it is robust in the presence of noise and occlusion. Furthermore, we kernelize the algorithm to handle the non-linearities present in video data. Numerous experiments using unconstrained video sequences show that our method is effective and performs significantly better than many state-ofthe-art video-based face recognition algorithms in the literature. I. INTRODUCTIONThough face recognition research [1] has traditionally concentrated on recognition from still images, recently, videobased face recognition has also gained a lot of traction. Faces are essentially articulating three dimensional objects. For faces, cues from motion possesses useful information in the form of behavioral traits such as idiosyncratic head movements and gestures, which can potentially aid in recognition tasks. Humans efficiently fuse face, body, and motion when recognizing people in video [2]. From video sequence effective representations such as three dimensional face models or super-resolved frames can be estimated. These techniques have the potential to improve recognition results.While the advantage of using motion information in face videos has been widely recognized, computational models for video-based face recognition have only recently gained attention. In this paper, we consider the problem of video-tovideo face recognition where one is presented with a video sequence and the goal is to recognize the person in the video. A key challenge is exploiting the extra information available in a video. In addition, different video sequences of the same subject may contain variations in resolution, illumination, pose, and facial expressions. These variations contribute to the difficulties in designing an effective video-based face recognition algorithm.

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“…To compare these images we use fisherfaces algorithm. [ 3,6 ] For this process we first import the module, then read the dataset. Now create the model default parameters.…”

Section: B Fisherfacesmentioning

confidence: 99%

Smart Reconnaisance and Apprise System using Raspberry PI

Alwar¹

2018

IJRASET

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In day today life, the security of our belonging is becoming more important. The modern surveillance is integrated with many automation technologies. In this modern world, crime has become ultra modern tool. At this point of time a lot of incident occurs like robbery, stealing unwanted entrance happens, threatening abruptly. So the security does matters in this daily life. This paper presents the monitoring and controlling of surveillance system enhanced with WI-FI (IEEE 802.11) technology. This system consists of webcam, Raspberry PI (BCM2837).In this system, webcam is used to detect the motions or to trace out the intruders and also detect fire. In case of any human movement or fire accident occurs, the webcam will capture live data in the surroundings and transmit it to authorized user through WI-FI(IEEE 802.11). Simultaneously, the buzzer alerts the nearby neighbors. The system also consist of sprayer, sprays the chloroform liquid on the intruders.

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Remote identification of faces: Problems, prospects, and progress

Cited by 32 publications

References 41 publications

Subspace Interpolation via Dictionary Learning for Unsupervised Domain Adaptation

Subspace Interpolation via Dictionary Learning for Unsupervised Domain Adaptation

Video-based face recognition via joint sparse representation

Smart Reconnaisance and Apprise System using Raspberry PI

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