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How Colorful Should Faces Be? Harmonizing Color and Model Quantization for Resource-restricted Face Recognition

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How Colorful Should Faces Be? Harmonizing Color and Model Qu...

IEEE International Joint Conference on Biometrics (IJCB)

作者： Jan Niklas Kolf Jurek Elliesen Fadi Boutros Naser Damer Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

State-of-the-art face recognition (FR) systems are based on overparameterized deep neural networks (DNN) which commonly use face images with 256 3 colors. The use of DNN and the storage of face images as references for comparison are limited in resource-restricted domains, which are hemmed in storage and computational capacity. A possible solution is to store the image only as a feature, which renders the human evaluation of the image impossible and forces the use of a single DNN (vendor) across systems. In this paper, we present a novel study on the possibility and effect of image color quantization on FR performance and storage efficiency. We leverage our conclusions to propose harmonizing the color quantization with the low-bit quantization of FR models. This combination significantly reduces the bits required to represent both the image and the FR model. In an extensive experiment on diverse sets of DNN architectures and color quantization steps, we validate on multiple benchmarks that the proposed methodology can successfully reduce the number of bits required for image pixels and DNN data while maintaining nearly equal recognition rates. The code and pre-trained models are available at https://***/jankolf/ColorQuantization.

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SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data

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SynthASpoof: Developing Face Presentation Attack Detection B...

IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Meiling Fang Marco Huber Naser Damer Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Recently, significant progress has been made in face presentation attack detection (PAD), which aims to secure face recognition systems against presentation attacks, owing to the availability of several face PAD datasets. However, all available datasets are based on privacy and legally-sensitive authentic biometric data with a limited number of subjects. To target these legal and technical challenges, this work presents the first synthetic-based face PAD dataset, named SynthASpoof, as a large-scale PAD development dataset. The bona fide samples in SynthASpoof are synthetically generated and the attack samples are collected by presenting such synthetic data to capture systems in a real attack scenario. The experimental results demonstrate the feasibility of using SynthASpoof for the development of face PAD. Moreover, we boost the performance of such a solution by incorporating the domain generalization tool MixStyle into the PAD solutions. Additionally, we showed the viability of using synthetic data as a supplement to enrich the diversity of limited authentic training data and consistently enhance PAD performances. The SynthASpoof dataset, containing 25,000 bona fide and 78,800 attack samples, the implementation, and the pre-trained weights are made publicly available 1 .

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CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability

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CR-FIQA: Face Image Quality Assessment by Learning Sample Re...

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Fadi Boutros Meiling Fang Marcel Klemt Biying Fu Naser Damer Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Face image quality assessment (FIQA) estimates the utility of the captured image in achieving reliable and accurate recognition performance. This work proposes a novel FIQA method, CR-FIQA, that estimates the face image quality of a sample by learning to predict its relative classifiability. This classifiability is measured based on the allocation of the training sample feature representation in angular space with respect to its class center and the nearest negative class center. We experimentally illustrate the correlation between the face image quality and the sample relative classifiability. As such property is only observable for the training dataset, we propose to learn this property by probing internal network observations during the training process and utilizing it to predict the quality of unseen samples. Through extensive evaluation experiments on eight benchmarks and four face recognition models, we demonstrate the superiority of our proposed CR-FIQA over state-of-the-art (SOTA) FIQA algorithms. 1 1 https://***/fdbtrs/CR-FIQA

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IDiff-Face: Synthetic-based Face Recognition through Fizzy Identity-Conditioned Diffusion Models

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arXiv 2023年

作者： Boutros, Fadi Grebe, Jonas Henry Kuijper, Arjan Damer, Naser Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

The availability of large-scale authentic face databases has been crucial to the significant advances made in face recognition research over the past decade. However, legal and ethical concerns led to the recent retraction of many of these databases by their creators, raising questions about the continuity of future face recognition research without one of its key resources. Synthetic datasets have emerged as a promising alternative to privacy-sensitive authentic data for face recognition development. However, recent synthetic datasets that are used to train face recognition models suffer either from limitations in intra-class diversity or cross-class (identity) discrimination, leading to less optimal accuracies, far away from the accuracies achieved by models trained on authentic data. This paper targets this issue by proposing IDiff-Face, a novel approach based on conditional latent diffusion models for synthetic identity generation with realistic identity variations for face recognition training. Through extensive evaluations, our proposed synthetic-based face recognition approach pushed the limits of state-of-the-art performances, achieving, for example, 98.00% accuracy on the Labeled Faces in the Wild (LFW) benchmark, far ahead from the recent synthetic-based face recognition solutions with 95.40% and bridging the gap to authentic-based face recognition with 99.82% accuracy* © 2023, CC BY-NC-SA.

关键词： Face recognition

Face Presentation Attack Detection by Excavating Causal Clues and Adapting Embedding Statistics

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arXiv 2023年

作者： Fang, Meiling Damer, Naser Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Recent face presentation attack detection (PAD) leverages domain adaptation (DA) and domain generalization (DG) techniques to address performance degradation on unknown domains. However, DA-based PAD methods require access to unlabeled target data, while most DG-based PAD solutions rely on a priori, i.e., known domain labels. Moreover, most DA-/DG-based methods are computationally intensive, demanding complex model architectures and/or multi-stage training processes. This paper proposes to model face PAD as a compound DG task from a causal perspective, linking it to model optimization. We excavate the causal factors hidden in the high-level representation via counterfactual intervention. Moreover, we introduce a class-guided MixStyle to enrich feature-level data distribution within classes instead of focusing on domain information. Both class-guided MixStyle and counterfactual intervention components introduce no extra trainable parameters and negligible computational resources. Extensive cross-dataset and analytic experiments demonstrate the effectiveness and efficiency of our method compared to state-of-the-art PADs. The implementation and the trained weights are publicly available1 © 2023, CC BY-SA.

关键词： Face recognition

Are Explainability Tools Gender Biased? A Case Study on Face Presentation Attack Detection

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Are Explainability Tools Gender Biased? A Case Study on Face...

European Signal Processing Conference (EUSIPCO)

作者： Marco Huber Meiling Fang Fadi Boutros Naser Damer Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Face recognition (FR) systems continue to spread in our daily lives with an increasing demand for higher explain-ability and interpretability of FR systems that are mainly based on deep learning. While bias across demographic groups in FR systems has already been studied, the bias of explainability tools has not yet been investigated. As such tools aim at steering further development and enabling a better understanding of computer vision problems, the possible existence of bias in their outcome can lead to a chain of biased decisions. In this paper, we explore the existence of bias in the outcome of explainability tools by investigating the use case of face presentation attack detection. By utilizing two different explainability tools on models with different levels of bias, we investigate the bias in the outcome of such tools. Our study shows that these tools show clear signs of gender bias in the quality of their explanations.

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ExFaceGAN: Exploring Identity Directions in GAN’s Learned Latent Space for Synthetic Identity Generation

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arXiv 2023年

作者： Boutros, Fadi Klemt, Marcel Fang, Meiling Kuijper, Arjan Damer, Naser Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Deep generative models have recently presented impressive results in generating realistic face images of random synthetic identities. To generate multiple samples of a certain synthetic identity, several previous works proposed to disentangle the latent space of GANs by incorporating additional supervision or regularization, enabling the manipulation of certain attributes, e.g. identity, hairstyle, pose, or expression. Most of these works require designing special loss functions and training dedicated network architectures. Others proposed to disentangle specific factors in unconditional pretrained GANs latent spaces to control their output, which also requires supervision by attribute classifiers. Moreover, these attributes are entangled in GAN’s latent space, making it difficult to manipulate them without affecting the identity information. We propose in this work a framework, ExFaceGAN, to disentangle identity information in state-of-the-art pretrained GANs latent spaces, enabling the generation of multiple samples of any synthetic identity. Given a reference latent code of any synthetic image and latent space of pretrained GAN, our ExFaceGAN learns an identity directional boundary that disentangles the latent space into two sub-spaces, with latent codes of samples that are either identity similar or dissimilar to a reference image. By sampling from each side of the boundary, our ExFaceGAN can generate multiple samples of synthetic identity without the need for designing a dedicated architecture or supervision from attribute classifiers. The variations in our generated images are not limited to specific attributes as ExFaceGAN explicitly aims at disentangling identity information, while other visual attributes are randomly drawn from a learned GAN latent space. We demonstrate the generalizability and the effectiveness of ExFaceGAN by integrating it into learned latent spaces of three SOTA GAN approaches, StyleGAN-ADA, StyleGAN-3, and GAN-Control. As an example of

关键词： Network architecture

Identity-driven Three-Player Generative Adversarial Network for Synthetic-based Face Recognition

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arXiv 2023年

作者： Kolf, Jan Niklas Rieber, Tim Elliesen, Jurek Boutros, Fadi Kuijper, Arjan Damer, Naser Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Many of the commonly used datasets for face recognition development are collected from the internet without proper user consent. Due to the increasing focus on privacy in the social and legal frameworks, the use and distribution of these datasets are being restricted and strongly questioned. These databases, which have a realistically high variability of data per identity, have enabled the success of face recognition models. To build on this success and to align with privacy concerns, synthetic databases, consisting purely of synthetic persons, are increasingly being created and used in the development of face recognition solutions. In this work, we present a three-player generative adversarial network (GAN) framework, namely IDnet, that enables the integration of identity information into the generation process. The third player in our IDnet aims at forcing the generator to learn to generate identity-separable face images. We empirically proved that our IDnet synthetic images are of higher identity discrimination in comparison to the conventional two-player GAN, while maintaining a realistic intra-identity variation. We further studied the identity link between the authentic identities used to train the generator and the generated synthetic identities, showing very low similarities between these identities. We demonstrated the applicability of our IDnet data in training face recognition models by evaluating these models on a wide set of face recognition benchmarks. In comparison to the state-of-the-art works in synthetic-based face recognition, our solution achieved comparable results to a recent rendering-based approach and outperformed all existing GAN-based approaches. The training code and the synthetic face image dataset are publicly available. Copyright © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

IDiff-Face: Synthetic-based Face Recognition through Fizzy Identity-Conditioned Diffusion Models

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IDiff-Face: Synthetic-based Face Recognition through Fizzy I...

International Conference on computer Vision (ICCV)

作者： Fadi Boutros Jonas Henry Grebe Arjan Kuijper Naser Damer Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

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