Towards understanding the character of quality sampling in deep learning face recognition

Medvedev, Iurii; Tremoço, João; Mano, Beatriz; Santo, Luís Espírito; Gonçalves, Nuno

doi:10.1049/bme2.12095

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…Quality-based loss function adaptations have been intensively studied in recent works. Here the MagFace [32], QualFace [31,46] and AdaFace [25] share conceptual similarities of the approaches. All these losses indeed modify the marginal-based softmax in a sample-specific way.…”

Section: Face Recognitionmentioning

confidence: 99%

Young Labeled Faces in the Wild (YLFW): A Dataset for Children Faces Recognition

Medvedev¹,

Farhad²,

Gonçalves³

2023

Preprint

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Face recognition has achieved outstanding performance in the last decade with the development of deep learning techniques.Nowadays, the challenges in face recognition are related to specific scenarios, for instance, the performance under diverse image quality, the robustness for aging and edge cases of person age (children and elders), distinguishing of related identities.In this set of problems, recognizing children's faces is one of the most sensitive and important. One of the reasons for this problem is the existing bias towards adults in existing face datasets.In this work, we present a benchmark dataset for children's face recognition, which is compiled similarly to the famous face recognition benchmarks LFW, CALFW, CPLFW, XQLFW and AgeDB. We also present a development dataset (separated into train and test parts) for adapting face recognition models for face images of children. The proposed data is balanced for African, Asian, Caucasian, and Indian races. To the best of our knowledge, this is the first standartized data tool set for benchmarking and the largest collection for development for children's face recognition. Several face recognition experiments are presented to demonstrate the performance of the proposed data tool set.

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Section: Face Recognitionmentioning

confidence: 99%

Young Labeled Faces in the Wild (YLFW): A Dataset for Children Faces Recognition

Medvedev¹,

Farhad²,

Gonçalves³

2023

Preprint

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“…To filter such images, and to measure the quality and the recognizability of faces, one could use face quality estimation methods [21] such as CR-FIQA [37], FaceQAN [38], L2RT-FIQA [39], DifFIQA [40] and others [27], [41]- [48]. Sometimes these methods are inserted in the process of face representation learning [19], [23], [49]- [55], improving the results with more precise training signals. However, none of these methods are used in the Prototype Memory-based face representation learning.…”

Section: B Face Quality Estimationmentioning

confidence: 99%

Prototype Memory for Large-Scale Face Representation Learning

Smirnov¹,

Garaev²,

Galyuk³

2022

IEEE Access

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Face representation learning using datasets with a massive number of identities requires appropriate training methods. Softmax-based approach, currently the state-of-the-art in face recognition, in its usual "full softmax" form is not suitable for datasets with millions of persons. Several methods, based on the "sampled softmax" approach, were proposed to remove this limitation. These methods, however, have a set of disadvantages. One of them is a problem of "prototype obsolescence": classifier weights (prototypes) of the rarely sampled classes, receive too scarce gradients and become outdated and detached from the current encoder state, resulting in incorrect training signals. This problem is especially serious in ultra-large-scale datasets. In this paper, we propose a novel face representation learning model called Prototype Memory, which alleviates this problem and allows training on a dataset of any size. Prototype Memory consists of the limited-size memory module for storing recent class prototypes and employs a set of algorithms to update it in appropriate way. New class prototypes are generated on the fly using exemplar embeddings in the current mini-batch. These prototypes are enqueued to the memory and used in a role of classifier weights for softmax classification-based training. To prevent obsolescence and keep the memory in close connection with the encoder, prototypes are regularly refreshed, and oldest ones are dequeued and disposed of. Prototype Memory is computationally efficient and independent of dataset size. It can be used with various loss functions, hard example mining algorithms and encoder architectures. We prove the effectiveness of the proposed model by extensive experiments on popular face recognition benchmarks.

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Young Labeled Faces in the Wild (YLFW): A Dataset for Children Faces Recognition

Medvedev,

Shadmand,

Gonçalves

2024

2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG)

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Towards understanding the character of quality sampling in deep learning face recognition

Cited by 6 publications

References 27 publications

Young Labeled Faces in the Wild (YLFW): A Dataset for Children Faces Recognition

Young Labeled Faces in the Wild (YLFW): A Dataset for Children Faces Recognition

Prototype Memory for Large-Scale Face Representation Learning

Young Labeled Faces in the Wild (YLFW): A Dataset for Children Faces Recognition

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