Distributed Face Recognition Based on Load Balancing and Dynamic Prediction

Zou, Fangyuan; Li, Jing; Min, Weidong

doi:10.3390/app9040794

Cited by 5 publications

(3 citation statements)

References 45 publications

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“…Face detection is the key step in many different face-related applications and studies [19]. Most of the early work was designed for high-resolution images and large targets by using statistical learning methods to automatically extract features.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Pre-Identification and Cascaded Detection for Tiny Faces

Yang

Min

et al. 2019

Applied Sciences

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Although the face detection problem has been studied for decades, searching tiny faces in the whole image is still a challenging task, especially in low-resolution images. Traditional face detection methods are based on hand-crafted features, but the features of tiny faces are different from those of normal-sized faces, and thus the detection robustness cannot be guaranteed. In order to alleviate the problem in existing methods, we propose a pre-identification mechanism and a cascaded detector (PMCD) for tiny-face detection. This pre-identification mechanism can greatly reduce background and other irrelevant information. The cascade detector is designed with two stages of deep convolutional neural network (CNN) to detect tiny faces in a coarse-to-fine manner, i.e., the face-area candidates are pre-identified as region of interest (RoI) based on a real-time pedestrian detector and the pre-identification mechanism, the set of RoI candidates is the input of the second sub-network instead of the whole image. Benefiting from the above mechanism, the second sub-network is designed as a shallow network which can keep high accuracy and real-time performance. The accuracy of PMCD is at least 4% higher than the other state-of-the-art methods on detecting tiny faces, while keeping real-time performance.

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Section: Related Workmentioning

confidence: 99%

Real-Time Pre-Identification and Cascaded Detection for Tiny Faces

Yang

Min

et al. 2019

Applied Sciences

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“…Kumar [17] identification system obtained a high frontal face detection rate, the study only looked at the detection rate and not the whole system's efficiency. Some work on boosting face recognition performance in parallel was published in papers [15], [18], [19]. Instead of focusing on the number of errors, ours dramatically increased the identification speed like the other systems.…”

Section: Introductionmentioning

confidence: 99%

A parallel algorithm of multiple face detection on multi-core system

Al-Neama

Alshiha²,

Saeed³

2023

IJEECS

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<p><span lang="EN-US">This work offers a graphics processing unit (GPU)-based system for real-time face recognition, which can detect and identify faces with high accuracy. This work created and implemented novel parallel strategies for image integral, computation scan window processing, and classifier amplification and correction as part of the face identification phase of the Viola-Jones cascade classifier. Also, the algorithm and parallelized a portion of the testing step during the facial recognition stage were experimented with. The suggested approach significantly improves existing facial recognition methods by enhancing the performance of two crucial components. The experimental findings show that the proposed method, when implemented on an NVidia GTX 570 graphics card, outperforms the typical CPU program by a factor of 19.72 in the detection phase and 1573 in the recognition phase, with only 2000 images trained and 40 images tested. The recognition rate will plateau when the hardware's capabilities are maxed out. This demonstrates that the suggested method works well in real-time.</span></p>

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“…The technology presents an emerging challenge due to its large-area occlusion, complex data, high computational complexity and strict requirements on the spatio-temporal consistency of the recognition effect. Recent works focus on exploring different methods to detect or recognize faces, ranging from that general full face recognition [1][2][3], arbitrary occlusion face recognition [4][5][6], to masked face recognition [7,8]. Among these methods, occlusion or masked recognition needs to address some problems such as limited features, training cost or unstable recognition rate.…”

Section: Introductionmentioning

confidence: 99%

AFF-Net: a masked face recognition network based on attention and feature fusion

2023

Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023)

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The context of the COVID-19 pandemic has made an increasing number of people accustomed to wearing masks to avoid getting infected. This phenomenon leads to an urgent demand for face recognition with masks, which poses serious challenge for the accuracy of face recognition. This paper seeks to produce a model called AFF-Net based on an attention mechanism and feature fusion and self-attention to address the masked face recognition by designing four compact modules to achieve the optimization of the network structure to solve the problem of unstable recognition rate and the low model generalization ability. Specifically, we fuse the features of different layers in the improved full connected module to assign weights to the channels, and formulate a specific training strategy to improve the feature learning performance. Experiments on representative datasets indicate that the proposed method can outperform the competing method significantly while maintaining high real-time efficiency of masked face recognition.

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Distributed Face Recognition Based on Load Balancing and Dynamic Prediction

Cited by 5 publications

References 45 publications

Real-Time Pre-Identification and Cascaded Detection for Tiny Faces

Real-Time Pre-Identification and Cascaded Detection for Tiny Faces

A parallel algorithm of multiple face detection on multi-core system

AFF-Net: a masked face recognition network based on attention and feature fusion

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