The MegaFace Benchmark: 1 Million Faces for Recognition at Scale

Kemelmacher-Shlizerman, Ira; Seitz, Steven M.; Miller, Delbert C.; Brossard, Evan

doi:10.1109/cvpr.2016.527

Cited by 794 publications

(594 citation statements)

References 41 publications

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“…However, the MegaFace Challenge [51] does start to investigate what happens to the performance of face recognition algorithms when the person to be recognized is mixed with up to a million distractors that were not in the training set. It was found that all algorithms had lower recognition accuracy when they were tested on the MegaFace dataset.…”

Section: Discussionmentioning

confidence: 99%

NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification

et al. 2016

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Near-infrared (NIR) face recognition has attracted increasing attention because of its advantage of illumination invariance. However, traditional face recognition methods based on NIR are designed for and tested in cooperative-user applications. In this paper, we present a convolutional neural network (CNN) for NIR face recognition (specifically face identification) in non-cooperative-user applications. The proposed NIRFaceNet is modified from GoogLeNet, but has a more compact structure designed specifically for the Chinese Academy of Sciences Institute of Automation (CASIA) NIR database and can achieve higher identification rates with less training time and less processing time. The experimental results demonstrate that NIRFaceNet has an overall advantage compared to other methods in the NIR face recognition domain when image blur and noise are present. The performance suggests that the proposed NIRFaceNet method may be more suitable for non-cooperative-user applications.

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Section: Discussionmentioning

confidence: 99%

NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification

et al. 2016

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“…This is exactly the type of problem that an officer wearing the advanced vest would be faced with in trying to use facial recognition in the field. They found that those algorithms trained on larger sets of data performed better (Kemelmacher-Shlizerman et al, 2016). However, this is still an active field of research.…”

Section: Performance Of Facial Recognition Systemsmentioning

confidence: 99%

Wearable Technologies for Law Enforcement: Multifunctional Vest System Options

Silberglitt¹,

Lauland²,

Watson³

et al. 2017

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“…Despite promising performance of automatic face recognition algorithms in a controlled setting (Kemelmacher-Shlizerman et al, 2015), many applications require accurate identification at planetary scale, i.e., finding the best matching face in a database of millions of people. For instance, face recognition algorithms failed to identify criminals in the Boston marathon bombing (Klontz et al, 2013).…”

Section: The Role Of Biometric Technology In Forensicsmentioning

confidence: 99%

Leveraging Biometrics for Insider Misuse Identification

Alruban¹,

Clarke²,

Li³

et al. 2016

IJCSA

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Insider misuse has become a real threat to many enterprises in the last decade. A major source of such threats originates from those individuals who have inside knowledge about the organization's resources. Therefore, preventing or responding to such incidents has become a challenging task. Digital forensics has grown into a de-facto standard in the examination of electronic evidence, which provides a basis for investigating incidents. A key barrier however is often being able to associate an individual to the stolen data-especially when stolen credentials and the Trojan defense are two commonly cited arguments. This paper proposes an approach that can more inextricably link the use of information (e.g. images, documents and emails) to the individual users who use and access them through the use of transparent biometric imprinting. The use of transparent biometrics enables the covert capture of a user's biometric information-avoiding the potential for forgery. A series of experiments are presented to evaluate the capability of retrieving the biometric information through a variety of file modification attacks. The preliminary feasibility study has shown that it is possible to correlate an individual's biometric information with a digital object (images) and still be able to recover the biometric signal even with significant file modification.

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The MegaFace Benchmark: 1 Million Faces for Recognition at Scale

Cited by 794 publications

References 41 publications

NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification

NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification

Wearable Technologies for Law Enforcement: Multifunctional Vest System Options

Leveraging Biometrics for Insider Misuse Identification

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