Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A

Klare, Brendan; Klein, Ben; Taborsky, Emma; Blanton, Austin; Cheney, Jordan; Allen, Kristen; Grother, Patrick J.; Mah, Alan; Burge, Mark J.; Jain, Anil K.

doi:10.1109/cvpr.2015.7298803

Cited by 639 publications

(603 citation statements)

References 10 publications

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“…Similar large-scale databases for facial landmark localisation include 300 W (Sagonas et al 2013b) LFPW (Belhumeur et al 2013), AFLW (Köstinger et al 2011) and HELEN (Le et al 2012). Similarly, for face recognition there exists LFW (Huang et al 2007), FRVT (Phillips et al 2000) and the recently introduced Janus database (IJB-A) (Klare et al 2015). -The establishment of in-the-wild benchmarks and challenges that provide a fair comparison between state of the art techniques.…”

Section: Introductionmentioning

confidence: 99%

“…-The establishment of in-the-wild benchmarks and challenges that provide a fair comparison between state of the art techniques. FDDB (Jain and Learned-Miller 2010), 300 W (Sagonas et al 2013a(Sagonas et al , 2015 and Janus (Klare et al 2015) are the most characteristic examples for face detection, facial landmark localisation and face recognition, respectively.…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

et al. 2017

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Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as "in-the-wild"). This is partially attributed to the fact that comprehensive "in-the-wild" benchmarks have been developed for face detection, landmark localisation and recognition/verification. A very important technology that has not been thoroughly evaluated yet is deformable face tracking "in-the-wild". Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos. In this paper, we perform the first, to the best of our knowledge, thorough evaluation of state-of-theart deformable face tracking pipelines using the recently introduced 300 VW benchmark. We evaluate many different architectures focusing mainly on the task of on-line deformable face tracking. In particular, we compare the following general strategies: (a) generic face detection plus generic facial landmark localisation, (b) generic model free tracking plus generic facial landmark localisation, as well as (c) hybrid approaches using state-of-the-art face detection, model free tracking and facial landmark localisation technologies. Our evaluation reveals future avenues for further research on the topic.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

et al. 2017

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“…Face recognition algorithms generally try to address two problems -identify verification and subject identification. Face verification, as known as the 1 : 1 matching problem [11], answers the question, are these two people actually the same?, while face identification, also known as the 1 : N problem [11], answers the question, who is this person, given a database of faces?…”

Section: Introductionmentioning

confidence: 99%

“…However, face recognition problem is far from solved, especially in an uncontrolled environment with extreme pose, illumination, expression and age variations. Indeed, as discussed in [11], state-of-the-art commercial and open-source face recognition systems performed far less than satisfactory on the recently released National Institute of Standards and Technology's (NIST) IARPA Janus Benchmark-A (IJB-A), which is considered much more challenging, than LFW, in terms of the variability in pose, illumination, expression, aging, resolution, etc. [19,15,5].…”

Section: Introductionmentioning

confidence: 99%

Face recognition using deep multi-pose representations

AbdAlmageed

Rawls

et al. 2016

2016 IEEE Winter Conference on Applications of Computer Vision (WACV)

134

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We introduce our method and system for face recognition using multiple pose-aware deep learning models. In our representation, a face image is processed by several posespecific deep convolutional neural network (CNN) models to generate multiple pose-specific features. 3D rendering is used to generate multiple face poses from the input image. Sensitivity of the recognition system to pose variations is reduced since we use an ensemble of pose-specific CNN features. The paper presents extensive experimental results on the effect of landmark detection, CNN layer selection and pose model selection on the performance of the recognition pipeline. Our novel representation achieves better results than the state-of-the-art on IARPA's CS2 and NIST's IJB-A in both verification and identification (i.e. search) tasks.

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“…This means that when the tips within an impression could be recognized correctly, the objects all can be located and simple attributes such as edge, spot, and form may be tested [1], [6]- [10].…”

Section: Introductionmentioning

confidence: 99%

Limits those are invisible to be disclosed by using new imageprocessing Techniques

Goyal¹,

Verma²,

Mishra³

2017

IJISSE

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Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A

Cited by 639 publications

References 10 publications

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

Face recognition using deep multi-pose representations

Limits those are invisible to be disclosed by using new imageprocessing Techniques

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