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Dong Wang, Jia Guo, Qiqi Shao, Haochi He, Zhian Chen, Chuanbao Xiao, et al. (2023). Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 6379–6390).
Abstract: Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems. Despite substantial advancements, the generalization of existing approaches to real-world applications remains challenging. This limitation can be attributed to the scarcity and lack of diversity in publicly available FAS datasets, which often leads to overfitting during training or saturation during testing. In terms of quantity, the number of spoof subjects is a critical determinant. Most datasets comprise fewer than 2,000 subjects. With regard to diversity, the majority of datasets consist of spoof samples collected in controlled environments using repetitive, mechanical processes. This data collection methodology results in homogenized samples and a dearth of scenario diversity. To address these shortcomings, we introduce the Wild Face Anti-Spoofing (WFAS) dataset, a large-scale, diverse FAS dataset collected in unconstrained settings. Our dataset encompasses 853,729 images of 321,751 spoof subjects and 529,571 images of 148,169 live subjects, representing a substantial increase in quantity. Moreover, our dataset incorporates spoof data obtained from the internet, spanning a wide array of scenarios and various commercial sensors, including 17 presentation attacks (PAs) that encompass both 2D and 3D forms. This novel data collection strategy markedly enhances FAS data diversity. Leveraging the WFAS dataset and Protocol 1 (Known-Type), we host the Wild Face Anti-Spoofing Challenge at the CVPR2023 workshop. Additionally, we meticulously evaluate representative methods using Protocol 1 and Protocol 2 (Unknown-Type). Through an in-depth examination of the challenge outcomes and benchmark baselines, we provide insightful analyses and propose potential avenues for future research. The dataset is released under Insightface 1 .
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Yaxing Wang, Abel Gonzalez-Garcia, Luis Herranz, & Joost Van de Weijer. (2021). Controlling biases and diversity in diverse image-to-image translation. CVIU - Computer Vision and Image Understanding, 202, 103082.
Abstract: JCR 2019 Q2, IF=3.121
The task of unpaired image-to-image translation is highly challenging due to the lack of explicit cross-domain pairs of instances. We consider here diverse image translation (DIT), an even more challenging setting in which an image can have multiple plausible translations. This is normally achieved by explicitly disentangling content and style in the latent representation and sampling different styles codes while maintaining the image content. Despite the success of current DIT models, they are prone to suffer from bias. In this paper, we study the problem of bias in image-to-image translation. Biased datasets may add undesired changes (e.g. change gender or race in face images) to the output translations as a consequence of the particular underlying visual distribution in the target domain. In order to alleviate the effects of this problem we propose the use of semantic constraints that enforce the preservation of desired image properties. Our proposed model is a step towards unbiased diverse image-to-image translation (UDIT), and results in less unwanted changes in the translated images while still performing the wanted transformation. Experiments on several heavily biased datasets show the effectiveness of the proposed techniques in different domains such as faces, objects, and scenes.
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Jun Wan, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, & Stan Z Li. (2023). Best Solutions Proposed in the Context of the Face Anti-spoofing Challenge Series. In Advances in Face Presentation Attack Detection (37–78).
Abstract: The PAD competitions we organized attracted more than 835 teams from home and abroad, most of them from the industry, which shows that the topic of face anti-spoofing is closely related to daily life, and there is an urgent need for advanced algorithms to solve its application needs. Specifically, the Chalearn LAP multi-modal face anti-spoofing attack detection challenge attracted more than 300 teams for the development phase with a total of 13 teams qualifying for the final round; the Chalearn Face Anti-spoofing Attack Detection Challenge attracted 340 teams in the development stage, and finally, 11 and 8 teams have submitted their codes in the single-modal and multi-modal face anti-spoofing recognition challenges, respectively; the 3D High-Fidelity Mask Face Presentation Attack Detection Challenge attracted 195 teams for the development phase with a total of 18 teams qualifying for the final round. All the results were verified and re-run by the organizing team, and the results were used for the final ranking. In this chapter, we briefly the methods developed by the teams participating in each competition, and introduce the algorithm details of the top-three ranked teams in detail.
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Jun Wan, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, & Stan Z Li. (2023). Face Anti-spoofing Progress Driven by Academic Challenges. In Advances in Face Presentation Attack Detection (1–15). SLCV.
Abstract: With the ubiquity of facial authentication systems and the prevalence of security cameras around the world, the impact that facial presentation attack techniques may have is huge. However, research progress in this field has been slowed by a number of factors, including the lack of appropriate and realistic datasets, ethical and privacy issues that prevent the recording and distribution of facial images, the little attention that the community has given to potential ethnic biases among others. This chapter provides an overview of contributions derived from the organization of academic challenges in the context of face anti-spoofing detection. Specifically, we discuss the limitations of benchmarks and summarize our efforts in trying to boost research by the community via the participation in academic challenges
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Jun Wan, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, & Stan Z Li. (2023). Face Presentation Attack Detection (PAD) Challenges. In Advances in Face Presentation Attack Detection (17–35). SLCV.
Abstract: In recent years, the security of face recognition systems has been increasingly threatened. Face Anti-spoofing (FAS) is essential to secure face recognition systems primarily from various attacks. In order to attract researchers and push forward the state of the art in Face Presentation Attack Detection (PAD), we organized three editions of Face Anti-spoofing Workshop and Competition at CVPR 2019, CVPR 2020, and ICCV 2021, which have attracted more than 800 teams from academia and industry, and greatly promoted the algorithms to overcome many challenging problems. In this chapter, we introduce the detailed competition process, including the challenge phases, timeline and evaluation metrics. Along with the workshop, we will introduce the corresponding dataset for each competition including data acquisition details, data processing, statistics, and evaluation protocol. Finally, we provide the available link to download the datasets used in the challenges.
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Jun Wan, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, & Stan Z Li. (2023). Advances in Face Presentation Attack Detection.
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Jun Wan, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, & Stan Z. Li. (2020). Multi-modal Face Presentation Attach Detection (Vol. 13).
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Yaxing Wang, Abel Gonzalez-Garcia, David Berga, Luis Herranz, Fahad Shahbaz Khan, & Joost Van de Weijer. (2020). MineGAN: effective knowledge transfer from GANs to target domains with few images. In 33rd IEEE Conference on Computer Vision and Pattern Recognition.
Abstract: One of the attractive characteristics of deep neural networks is their ability to transfer knowledge obtained in one domain to other related domains. As a result, high-quality networks can be trained in domains with relatively little training data. This property has been extensively studied for discriminative networks but has received significantly less attention for generative models. Given the often enormous effort required to train GANs, both computationally as well as in the dataset collection, the re-use of pretrained GANs is a desirable objective. We propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs. This is done using a miner network that identifies which part of the generative distribution of each pretrained GAN outputs samples closest to the target domain. Mining effectively steers GAN sampling towards suitable regions of the latent space, which facilitates the posterior finetuning and avoids pathologies of other methods such as mode collapse and lack of flexibility. We perform experiments on several complex datasets using various GAN architectures (BigGAN, Progressive GAN) and show that the proposed method, called MineGAN, effectively transfers knowledge to domains with few target images, outperforming existing methods. In addition, MineGAN can successfully transfer knowledge from multiple pretrained GANs.
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Jun Wan, Sergio Escalera, Francisco Perales, & Josef Kittler. (2018). Articulated Motion and Deformable Objects. PR - Pattern Recognition, 79, 55–64.
Abstract: This guest editorial introduces the twenty two papers accepted for this Special Issue on Articulated Motion and Deformable Objects (AMDO). They are grouped into four main categories within the field of AMDO: human motion analysis (action/gesture), human pose estimation, deformable shape segmentation, and face analysis. For each of the four topics, a survey of the recent developments in the field is presented. The accepted papers are briefly introduced in the context of this survey. They contribute novel methods, algorithms with improved performance as measured on benchmarking datasets, as well as two new datasets for hand action detection and human posture analysis. The special issue should be of high relevance to the reader interested in AMDO recognition and promote future research directions in the field.
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Joost Van de Weijer, & Fahad Shahbaz Khan. (2015). An Overview of Color Name Applications in Computer Vision. In Computational Color Imaging Workshop.
Abstract: In this article we provide an overview of color name applications in computer vision. Color names are linguistic labels which humans use to communicate color. Computational color naming learns a mapping from pixels values to color names. In recent years color names have been applied to a wide variety of computer vision applications, including image classification, object recognition, texture classification, visual tracking and action recognition. Here we provide an overview of these results which show that in general color names outperform photometric invariants as a color representation.
Keywords: color features; color names; object recognition
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Joost Van de Weijer, & Fahad Shahbaz Khan. (2013). Fusing Color and Shape for Bag-of-Words Based Object Recognition. In 4th Computational Color Imaging Workshop (Vol. 7786, pp. 25–34). Springer Berlin Heidelberg.
Abstract: In this article we provide an analysis of existing methods for the incorporation of color in bag-of-words based image representations. We propose a list of desired properties on which bases fusing methods can be compared. We discuss existing methods and indicate shortcomings of the two well-known fusing methods, namely early and late fusion. Several recent works have addressed these shortcomings by exploiting top-down information in the bag-of-words pipeline: color attention which is motivated from human vision, and Portmanteau vocabularies which are based on information theoretic compression of product vocabularies. We point out several remaining challenges in cue fusion and provide directions for future research.
Keywords: Object Recognition; color features; bag-of-words; image classification
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P. Wang, V. Eglin, C. Garcia, C. Largeron, Josep Llados, & Alicia Fornes. (2014). Représentation par graphe de mots manuscrits dans les images pour la recherche par similarité. In Colloque International Francophone sur l'Écrit et le Document (pp. 233–248).
Abstract: Effective information retrieval on handwritten document images has always been
a challenging task. In this paper, we propose a novel handwritten word spotting approach based on graph representation. The presented model comprises both topological and morphological signatures of handwriting. Skeleton-based graphs with the Shape Context labeled vertexes are established for connected components. Each word image is represented as a sequence of graphs. In order to be robust to the handwriting variations, an exhaustive merging process based on DTW alignment results introduced in the similarity measure between word images. With respect to the computation complexity, an approximate graph edit distance approach using bipartite matching is employed for graph matching. The experiments on the George Washington dataset and the marriage records from the Barcelona Cathedral dataset demonstrate that the proposed approach outperforms the state-of-the-art structural methods.
Keywords: word spotting; graph-based representation; shape context description; graph edit distance; DTW; block merging; query by example
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P. Wang, V. Eglin, C. Garcia, C. Largeron, Josep Llados, & Alicia Fornes. (2014). A Novel Learning-free Word Spotting Approach Based on Graph Representation. In 11th IAPR International Workshop on Document Analysis and Systems (pp. 207–211).
Abstract: Effective information retrieval on handwritten document images has always been a challenging task. In this paper, we propose a novel handwritten word spotting approach based on graph representation. The presented model comprises both topological and morphological signatures of handwriting. Skeleton-based graphs with the Shape Context labelled vertexes are established for connected components. Each word image is represented as a sequence of graphs. In order to be robust to the handwriting variations, an exhaustive merging process based on DTW alignment result is introduced in the similarity measure between word images. With respect to the computation complexity, an approximate graph edit distance approach using bipartite matching is employed for graph matching. The experiments on the George Washington dataset and the marriage records from the Barcelona Cathedral dataset demonstrate that the proposed approach outperforms the state-of-the-art structural methods.
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P. Wang, V. Eglin, C. Garcia, C. Largeron, Josep Llados, & Alicia Fornes. (2014). A Coarse-to-Fine Word Spotting Approach for Historical Handwritten Documents Based on Graph Embedding and Graph Edit Distance. In 22nd International Conference on Pattern Recognition (pp. 3074–3079).
Abstract: Effective information retrieval on handwritten document images has always been a challenging task, especially historical ones. In the paper, we propose a coarse-to-fine handwritten word spotting approach based on graph representation. The presented model comprises both the topological and morphological signatures of the handwriting. Skeleton-based graphs with the Shape Context labelled vertexes are established for connected components. Each word image is represented as a sequence of graphs. Aiming at developing a practical and efficient word spotting approach for large-scale historical handwritten documents, a fast and coarse comparison is first applied to prune the regions that are not similar to the query based on the graph embedding methodology. Afterwards, the query and regions of interest are compared by graph edit distance based on the Dynamic Time Warping alignment. The proposed approach is evaluated on a public dataset containing 50 pages of historical marriage license records. The results show that the proposed approach achieves a compromise between efficiency and accuracy.
Keywords: word spotting; coarse-to-fine mechamism; graphbased representation; graph embedding; graph edit distance
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Joost Van de Weijer, & Shida Beigpour. (2011). The Dichromatic Reflection Model: Future Research Directions and Applications. In José L. and B. Mestetskiy (Ed.), International Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. SciTePress.
Abstract: The dichromatic reflection model (DRM) predicts that color distributions form a parallelogram in color space, whose shape is defined by the body reflectance and the illuminant color. In this paper we resume the assumptions which led to the DRM and shortly recall two of its main applications domains: color image segmentation and photometric invariant feature computation. After having introduced the model we discuss several limitations of the theory, especially those which are raised once working on real-world uncalibrated images. In addition, we summerize recent extensions of the model which allow to handle more complicated light interactions. Finally, we suggest some future research directions which would further extend its applicability.
Keywords: dblp
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