Rate-energy-accuracy optimization of convolutional architectures for face recognition

Bondi, Luca; Baroffio, Luca; Cesana, Matteo; Tagliasacchi, Marco; Chiachia, Giovani; Rocha, Anderson

doi:10.1016/j.jvcir.2015.12.015

Cited by 5 publications

(4 citation statements)

References 28 publications

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“…Other researchers design compact and efficient network architectures, already tweaked to the limitations of low-powered devices. Bondi et al [31] analysed the resources used during the inner steps of the L3 + architecture and were able to propose optimisations, bearing in mind the ratio between ACC and energy consumption. The authors reduced by 94% the average energy consumption while maintaining a high ACC rate and low processing time.…”

Section: Mobile Effortsmentioning

confidence: 99%

Two‐tiered face verification with low‐memory footprint for mobile devices

et al. 2020

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Mobile devices have their popularity and affordability greatly increased in recent years. As a consequence of their ubiquity, these devices now carry all sorts of personal data that should be accessed only by their owner. Even though knowledge-based procedures are still the main methods to secure the owner's identity, recently biometric traits have been employed for more secure and effortless authentication. In this work, the authors propose a facial verification method optimised to the mobile environment. It consists of a two-tiered procedure that combines hand-crafted features and a convolutional neural network (CNN) to verify if the person depicted in a photograph corresponds to the device owner. To train a CNN for the verification task, the authors propose a hybrid-image input, which allows the network to process encoded information of a pair of face images. The proposed experiments show that the solution outperforms state of the art face verification methods, providing a 4× speedup when processing an image in recent smartphone models. Additionally, the authors show that the two-tiered procedure can be coupled with existing face verification CNNs improving their accuracy and efficiency. They also present a new data set of selfie pictures-RECOD Selfie data set-that hopefully will support future research in this scenario.

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Section: Mobile Effortsmentioning

confidence: 99%

Two‐tiered face verification with low‐memory footprint for mobile devices

et al. 2020

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“…Only one PRF and one hash function are employed in the BB-KDF, resulting in a much faster key generation with the BB-KDF than with the PB-KDF. Feature extraction time varies according to extraction algorithms and types of biometric data [57,58]. If we use a suitable algorithm for feature extraction, such as [16,59], the BB-KDF can be an…”

Section: Effects Of the Length Of Biometric Vectormentioning

confidence: 99%

Construction of a New Biometric-Based Key Derivation Function and Its Application

Seo

Park

Kim

et al. 2018

Security and Communication Networks

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Biometric data is user-identifiable and therefore methods to use biometrics for authentication have been widely researched. Biometric cryptosystems allow for a user to derive a cryptographic key from noisy biometric data and perform a cryptographic task for authentication or encryption. The fuzzy extractor is known as a prominent biometric cryptosystem. However, the fuzzy extractor has a drawback in that a user is required to store user-specific helper data or receive it online from the server with additional trusted channel, to derive a correct key. In this paper, we present a new biometric-based key derivation function (BB-KDF) to address the issues. In our BB-KDF, users are able to derive cryptographic keys solely from their own biometric data: users do not need any other user-specific helper information. We introduce a security model for the BB-KDF. We then construct the BB-KDF and prove its security in our security model. We then propose an authentication protocol based on the BB-KDF. Finally, we give experimental results to analyze the performance of the BB-KDF. We show that our proposed BB-KDF is computationally efficient and can be deployed on many different kinds of devices.

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“…With the goal of matching the capabilities of human vision [1], visual domain adaptation and generalisation on static images or video frames have received significant attention in recent years. In particular, surveillance systems rely more and more on multi-view learning in the case that the source domain is different from the target domain in visual recognition [2].…”

Section: Introductionmentioning

confidence: 99%

Real‐time face recognition based on pre‐identification and multi‐scale classification

Min

Fan

et al. 2019

IET Computer Vision

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In face recognition, searching a person's face in the whole picture is generally too time-consuming to ensure highdetection accuracy. Objects similar to the human face or multi-view faces in low-resolution images may result in the failure of face recognition. To alleviate the above problems, a real-time face recognition method based on pre-identification and multiscale classification is proposed in this study. The face area is segmented based on the proportion of human faces in the pedestrian area to reduce the search range, and faces can be robustly detected in complicated scenarios such as heads moving frequently or with large angles. To accurately recognise small-scale faces, the authors propose the multi-scale and multi-channel shallow convolution network, which combines a multi-scale mechanism on the feature map with a multi-channel convolution network for real-time face recognition. It performs face matching only in the pre-identified face areas instead of the whole image, therefore it is more efficient. Experimental results showed that the proposed real-time face recognition method detects and recognises faces correctly, and outperforms the existing methods in terms of effectiveness and efficiency.

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Rate-energy-accuracy optimization of convolutional architectures for face recognition

Cited by 5 publications

References 28 publications

Two‐tiered face verification with low‐memory footprint for mobile devices

Two‐tiered face verification with low‐memory footprint for mobile devices

Construction of a New Biometric-Based Key Derivation Function and Its Application

Real‐time face recognition based on pre‐identification and multi‐scale classification

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