A Quality Aware Sample-to-Sample Comparison for Face Recognition

Ebrahimi, Saadabadi, Mohammad Saeed; Malakshan, Sahar Rahimi; Zafari, Ali; Mostofa, Moktari; Nasrabadi, Nasser M.

doi:10.1109/wacv56688.2023.00607

Cited by 10 publications

(5 citation statements)

References 48 publications

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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%

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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obsolescence": classifier weights (prototypes) of the rarely sampled classes, receive too scarce gradients and become outdated and detached from the current encoder state, resulting in incorrect training signals.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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Section: B Face Quality Estimationmentioning

confidence: 99%

“…Information about the quality of face images could be used to filter unrecognizable faces and improve the performance of face recognition models. Face quality and recognizability labels also could be used for learning better face representations [19], [23] with more precise training signals and less noisy gradients.…”

Section: Introductionmentioning

confidence: 99%

Prototype Memory for Large-Scale Face Representation Learning

Smirnov¹,

Garaev²,

Galyuk³

2022

IEEE Access

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“…Automatic face recognition is a challenging task due to the involvement of several challenges such as illumination, pose, expressions, occlusion, and gender etc. Several methods have been developed for face recognition [2]. However, a good facial recognition system extracts key features from the face that are distinctive enough to separate the interclass variation as well as to reduce the intra class variation between the subjects at a low computational cost.…”

Section: Introductionmentioning

confidence: 99%

Synthetic Image Generation using Conditional GAN Provided Single Sample Face Image

ALI,

Jadoon,

Kim

2024

Preprint

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Facial recognition systems are widely used systems around the globe. Their applications in real world scenarios like law enforcement, surveillance, ID card identification, information security, access control and many more has made them very important in today’s world. These systems have shown promising results and excellent performance; however, these systems need tremendous number of facial images for training before they are implemented in the real-world scenarios. The problem arises when we face lack of training images due to which the performance of such systems decreases exponentially. The problem becomes even worse when there is only one image per subject for training our facial recognition system, the probe image might contain variation like illumination, expression and disguise which are hard to be accurately recognized. Generative models have achieved great success for handling the lack of training data by generating synthetic data that resembles the original data. We have created a new method by fine tuning the basic Conditional Generative Adversarial Network (CGAN) to generate synthetic facial images provided only single image per subject. These synthetic facial images contain six variations that are smile, anger, stare, illumination and disguise. We increased the size of the dataset and then applied a convolutional neural network for facial recognition, which improved the accuracy of our system from 0.76 to 0.99.

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“…Due to its significant applications, facial recognition has been a focal point of research for many years. The automatic recognition of faces is a complex task due to various challenges such as illumination, pose, expressions, occlusion, and demographic differences such as gender and race [2]. A wide range of deep learning-based methods [3,4] have been developed for face recognition.…”

Section: Introductionmentioning

confidence: 99%

Synthetic Image Generation Using Conditional GAN-Provided Single-Sample Face Image

Iqbal,

Jadoon,

Kim

2024

Applied Sciences

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The performance of facial recognition systems significantly decreases when faced with a lack of training images. This issue is exacerbated when there is only one image per subject available. Probe images may contain variations such as illumination, expression, and disguise, which are difficult to recognize accurately. In this work, we present a model that generates six facial variations from a single neutral face image. Our model is based on a CGAN, designed to produce six highly realistic facial expressions from one neutral face image. To evaluate the accuracy of our approach comprehensively, we employed several pre-trained models (VGG-Face, ResNet-50, FaceNet, and DeepFace) along with a custom CNN model. Initially, these models achieved only about 76% accuracy on single-sample neutral images, highlighting the SSPP challenge. However, after fine-tuning on the synthetic expressions generated by our CGAN from these single images, their accuracy increased significantly to around 99%. Our method has proven highly effective in addressing SSPP issues, as evidenced by the significant improvement achieved.

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A Quality Aware Sample-to-Sample Comparison for Face Recognition

Cited by 10 publications

References 48 publications

Prototype Memory for Large-Scale Face Representation Learning

Prototype Memory for Large-Scale Face Representation Learning

Synthetic Image Generation using Conditional GAN Provided Single Sample Face Image

Synthetic Image Generation Using Conditional GAN-Provided Single-Sample Face Image

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