Face recognition on smartphones via optimised Sparse Representation Classification

Shen, Yiran; Hu, Wen; Yang, Mingrui; Wei, Bo; Lucey, Simon; Chou, Chun Tung

doi:10.1109/ipsn.2014.6846756

Cited by 40 publications

(49 citation statements)

References 36 publications

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“…For example, Shen et al [48] optimize the projection matrix for the Sparse Representation Classification (SRC) and implement a fast and robust face recognition system on the mobile device. Glimpse's techniques are orthogonal and can be useful even for these client-only systems to hide the processing delay and reduce the amount of resources consumed.…”

Section: Related Workmentioning

confidence: 99%

Glimpse

Chen

Ravindranath

Deng

et al. 2015

Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems

244

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Glimpse is a continuous, real-time object recognition system for camera-equipped mobile devices. Glimpse captures full-motion video, locates objects of interest, recognizes and labels them, and tracks them from frame to frame for the user. Because the algorithms for object recognition entail significant computation, Glimpse runs them on server machines. When the latency between the server and mobile device is higher than a frame-time, this approach lowers objectrecognition accuracy. To regain accuracy, Glimpse uses an active cache of video frames on the mobile device. A subset of the frames in the active cache are used to track objects on the mobile, using (stale) hints about objects that arrive from the server from time to time. To reduce network bandwidth usage, Glimpse computes trigger frames to send to the server for recognizing and labeling. Experiments with Android smartphones and Google Glass over Verizon, AT&T, and a campus Wi-Fi network show that with hardware face detection support (available on many mobile devices), Glimpse achieves precision between 96.4% to 99.8% for continuous face recognition, which improves over a scheme performing hardware face detection and server-side recognition without Glimpse's techniques by between 1.8-2.5×. The improvement in precision for face recognition without hardware detection is between 1.6-5.5×. For road sign recognition, which does not have a hardware detector, Glimpse achieves precision between 75% and 80%; without Glimpse, continuous detection is non-functional (0.2%-1.9% precision). Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. Categories and Subject Descriptors

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Section: Related Workmentioning

confidence: 99%

Glimpse

Chen

Ravindranath

Deng

et al. 2015

Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems

244

View full text Add to dashboard Cite

show abstract

“…The techniques of using the smartphone's front camera for face recognition is suggested. Shen et al suggest an optimized Sparse Representation Classification for face authentication [2]. Sparse Representation Classification (SRC) is a state-of-the-art face-recognition algorithm, which has been shown to outperform many classical face-recognition algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Sparse Representation Classification (SRC) is a state-of-the-art face-recognition algorithm, which has been shown to outperform many classical face-recognition algorithms. The success of SRC is due to its use of 1 optimization, which makes SRC robust to noise and occlusions [2]. Because the former technique requires another step of authentication, Kang et al proposes a two-factor face authentication scheme using matrix transformations and a user password [3].…”

Section: Introductionmentioning

confidence: 99%

Increasing Trustworthiness of Face Authentication in Mobile Devices by Modeling Gesture Behavior and Location Using Neural Networks

2018

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Personal mobile devices currently have access to a significant portion of their user's private sensitive data and are increasingly used for processing mobile payments. Consequently, securing access to these mobile devices is a requirement for securing access to the sensitive data and potentially costly services. Face authentication is one of the promising biometrics-based user authentication mechanisms that has been widely available in this era of mobile computing. With a built-in camera capability on smartphones, tablets, and laptops, face authentication provides an attractive alternative of legacy passwords for its memory-less authentication process, which is so sophisticated that it can unlock the device faster than a fingerprint. Nevertheless, face authentication in the context of smartphones has proven to be vulnerable to attacks. In most current implementations, a sufficiently high-resolution face image displayed on another mobile device will be enough to circumvent security measures and bypass the authentication process. In order to prevent such bypass attacks, gesture recognition together with location is proposed to be additionally modeled. Gestures provide a faster and more convenient method of authentication compared to a complex password. The focus of this paper is to build a secure authentication system with face, location and gesture recognition as components. User gestures and location data are a sequence of time series; therefore, in this paper we propose to use unsupervised learning in the long short-term memory recurrent neural network to actively learn to recognize, group and discriminate user gestures and location. Moreover, a clustering-based technique is also implemented for recognizing gestures and location.

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“…Hadid et al [19] described the task of face detection and authentication in mobile phones, and experimentally analyse a face authentication scheme using Haar-like features with AdaBoost for face and eye detection and local binary pattern (LBP) approach for face authentication. Shen et al [20] address the challenges of performing face recognition accurately and efficiently on smartphones by designing a new face recognition algorithm called opti-sparse representation classification (opti-SRC). Sparse representation classification (SRC) is a state-of-the-art face recognition algorithm, which has been shown to outperform many classical face recognition algorithms in OpenCV.…”

Section: Introductionmentioning

confidence: 99%

The Geometry and Usage of the Supplementary Fisheye Lenses in Smartphones

Sahin¹

2017

Smartphones From an Applied Research Perspective

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Nowadays, mobile phones are more than a device that can only satisfy the communication need between people. Since fisheye lenses integrated with mobile phones are lightweight and easy to use, they are advantageous. In addition to this advantage, it is experimented whether fisheye lens and mobile phone combination can be used in a photogrammetric way, and if so, what will be the result. Fisheye lens equipment used with mobile phones was tested in this study. For this, standard calibration of 'Olloclip 3 in one' fisheye lens used with iPhone 4S mobile phone and 'Nikon FC-E9' fisheye lens used with Nikon Coolpix8700 are compared based on equidistant model. This experimental study shows that Olloclip 3 in one fisheye lens developed for mobile phones has at least the similar characteristics with classic fisheye lenses. The dimensions of fisheye lenses used with smart phones are getting smaller and the prices are reducing. Moreover, as verified in this study, the accuracy of fisheye lenses used in smartphones is better than conventional fisheye lenses. The use of smartphones with fisheye lenses will give the possibility of practical applications to ordinary users in the near future.

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Face recognition on smartphones via optimised Sparse Representation Classification

Cited by 40 publications

References 36 publications

Glimpse

Glimpse

Increasing Trustworthiness of Face Authentication in Mobile Devices by Modeling Gesture Behavior and Location Using Neural Networks

The Geometry and Usage of the Supplementary Fisheye Lenses in Smartphones

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