MLFW: A Database for Face Recognition on Masked Faces

Wang, Chengrui; Zhong, Yaoyao; Deng, Weihong

doi:10.1007/978-3-031-20233-9_18

Cited by 22 publications

(6 citation statements)

References 12 publications

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“…Datasets We use Celeb-A (Liu et al, 2015) for generating synthetic training data, LFW (Huang et al, 2007) for synthetic masked face evaluation, and RMFD (Huang et al, 2021) and MLFW (Wang et al, 2022) for real-world masked face evaluation. Celeb-A consists of 202,599 face images covering 10,177 celebrities.…”

Section: Methodsmentioning

confidence: 99%

Masked face recognition with convolutional visual self-attention network

Liu

et al. 2023

Neurocomputing

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Section: Methodsmentioning

confidence: 99%

Masked face recognition with convolutional visual self-attention network

Liu

et al. 2023

Neurocomputing

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“…These include LFW [22], which tests face recognition algorithms under real-world conditions, AgeDB-30 [24] for age invariance, CFP-FF and CFP-FP [25] for pose variations, and VGG2-FP [26] for frontal to profile comparisons. For extreme scenarios, we used CALFW [27] to assess real-world variations, CPLFW [28] for cross-pose recognition, MLFW [29] for ethnic diversity, SLLFW [30] for low-light conditions, and TALFW [31] for temporal aging variations. Additionally, RFW [7] provided fairness metrics by evaluating racial fairness across diverse racial groups (Africa-test, Asian-test, Caucasian-test, and Indian-test).…”

Section: Dataset and Evaluatementioning

confidence: 99%

“…On the LFW dataset [22], face recognition technologies based on ResNet and Transformer [23] have reached a performance saturation point. To thoroughly evaluate the effectiveness of our proposed improvements, we introduced verification datasets from multiple scenarios [7,22,[24][25][26][27][28][29][30][31] and measured the models' universality by calculating their average precision across these datasets. Much of the current research in face recognition focuses on Inception and its ResNet variants, demonstrating the possibility of achieving exceptional performance at lower computational costs by optimizing local network topology.…”

Section: Introductionmentioning

confidence: 99%

Face-Inception-Net for Recognition

Zhang,

Wang,

Zhang

et al. 2024

Electronics

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Face recognition in general scenarios has been saturated in recent years, but there is still room to enhance model performance in extreme scenarios and fairness situations. Inspired by the successful application of Transformer and ConvNet in computer vision, we propose a FIN-Block, which gives a more flexible composition paradigm for building a novel pure convolution model and provides a foundation for constructing a new framework for general face recognition in both extreme scenarios and fairness situations. FIN-Block-A uses a combination of stacked large-size convolution kernels and parallel convolution branches to ensure a large spatial receptive field while improving the module’s deep feature embedding and extraction capabilities. FIN-Block-B takes advantage of stacked orthogonal convolution kernels and parallel branches to balance model size and performance. By applying FIN-Block with an adapted convolution kernel size in different stages, we built a reasonable and novel framework Face-Inception-Net, and the performance of the model is highly competitive with ConvNeXt and InceptionNeXt. The models were trained on CASIA-WebFace and MS-wo-RFW databases and evaluated on 14 mainstream benchmarks, including LFW, extreme scene, and fairness test sets. The proposed Face-Inception-Net achieved the highest average TAR@FAR0.001 of 95.9% in all used benchmarks, fully demonstrating effectiveness and generality in various scenarios.

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“…To explain the need for a new dataset and justify the choice of the CNNs used in the experiments, we describe the features of the face databases available in the literature ( Section 2.1 ) and present the evolution of face recognition techniques over the years ( Section 2.2 ). Although several databases are available, including some for masked face recognition that has recently appeared [ 34 , 35 ], most do not include features adequate to evaluate the recognition performance in clips from security cameras, using as reference images sets of mugshots different from the frontal and profile pictures taken during the standard photo-signaling procedure. Nevertheless, CNN-based techniques demonstrated their superiority, where conditions such as lighting, facial expression, and pose are not fixed [ 15 , 28 ].…”

Section: Literature Reviewmentioning

confidence: 99%

FRMDB: Face Recognition Using Multiple Points of View

Contardo

Sernani

Tomassini

et al. 2023

Sensors

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Although face recognition technology is currently integrated into industrial applications, it has open challenges, such as verification and identification from arbitrary poses. Specifically, there is a lack of research about face recognition in surveillance videos using, as reference images, mugshots taken from multiple Points of View (POVs) in addition to the frontal picture and the right profile traditionally collected by national police forces. To start filling this gap and tackling the scarcity of databases devoted to the study of this problem, we present the Face Recognition from Mugshots Database (FRMDB). It includes 28 mugshots and 5 surveillance videos taken from different angles for 39 distinct subjects. The FRMDB is intended to analyze the impact of using mugshots taken from multiple points of view on face recognition on the frames of the surveillance videos. To validate the FRMDB and provide a first benchmark on it, we ran accuracy tests using two CNNs, namely VGG16 and ResNet50, pre-trained on the VGGFace and VGGFace2 datasets for the extraction of face image features. We compared the results to those obtained from a dataset from the related literature, the Surveillance Cameras Face Database (SCFace). In addition to showing the features of the proposed database, the results highlight that the subset of mugshots composed of the frontal picture and the right profile scores the lowest accuracy result among those tested. Therefore, additional research is suggested to understand the ideal number of mugshots for face recognition on frames from surveillance videos.

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MLFW: A Database for Face Recognition on Masked Faces

Cited by 22 publications

References 12 publications

Masked face recognition with convolutional visual self-attention network

Masked face recognition with convolutional visual self-attention network

Face-Inception-Net for Recognition

FRMDB: Face Recognition Using Multiple Points of View

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