Face spoofing dataset. The image is printed or displayed on a digital device.

 

Face spoofing dataset. To overcome these obstacles, we contribute a large-scale face anti-spoofing dataset, CelebA-Spoof , with the following appealing properties: 1) Quantity: CelebA-Spoof comprises of 625,537 pictures of 10,177 subjects, significantly larger than CelebA-Spoof is a large-scale face anti-spoofing dataset that has 625,537 images from 10,177 subjects, which includes 43 rich attributes on face, illumination, environment and spoof types. We collect and annotate spoof images of CelebA-Spoof. To keep consistent with the genuine face video, the standoff distance between spoofing medium and camera is also about 30-50 cm. Moreover, the face videos are captured with different backgrounds which guarantee the face videos are coupled with different illumination conditions. e. 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. Source: CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofing Biometric Attack dataset for the anti-spoofing task. - **Replay/video attack**: A more sophisticated way to trick the system, which usually The Replay-Attack Database for face spoofing consists of 1300 video clips of photo and video attack attempts to 50 clients, under different lighting conditions. The proposed approach exploits the color and texture information contained in genuine and spoofed faces, by considering the interrelation of their features using the Heterogeneous Auto-Similarities of Characteristics (HASC) descriptor (Biagio et al. 2 Datasets for Face Anti-Spoofing. Apr 12, 2023 · Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems. The images in the sample illustrate various spoofing techniques, such as printed photos (images 5-7), screen displays (images 3, 9-10), and genuine face images. For our experimentation, we focus on the most general ”intra” protocol, in which different spoof This review presents a face anti-spoofing database, which covers a huge range of different devices used for recording and for the video playback and it is hoped that database can serve as an evaluation platform for the future studies in the literature. , unlocking the device, signing in to some apps, confirming our payment, etc). This is inspired by Awesome-deep-vision , Awesome-adversarial-machine-learning , Awesome-deep-learning-papers , Awesome-NAS and Awesome-Pruing are becoming a inevitable threat [9, 30]. 31 million images of 9131 subjects (identities), with an average of 362. With the successful deployments in phone unlock, access control and e- Jul 7, 2022 · The authors fine-tuned a VGG-16 model that was pre-trained on ImageNet. Face anti-spoofing plays an important role in face recognition system to prevent security vulnerability. 3. from publication: An Anomaly Detection Approach to Face Spoofing Detection: A New Formulation and Evaluation Protocol | Face Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. To overcome these obstacles, we contribute a large-scale face anti-spoo ng dataset, CelebA-Spoof, with the following appealing properties: 1) Quantity: CelebA-Spoof comprises of 625,537 pictures of 10,177 subjects, signi cantly larger than the existing datasets. To the best of our knowledge, this is the first dataset to extend FAS research to real-world scenarios. All videos are generated by either having a (real) client trying to access a laptop through a built-in webcam or by displaying a photo or a video recording of the same client for at least 9 seconds. First, they all have the lim- 3D Passive Face Liveness Detection (Anti-Spoofing) & Deepfake detection. Dec 28, 2021 · This Face Anti Spoofing detector can be used in many different systems that needs realtime facial recognition with facial landmarks. CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations CelebA-Spoof：大规模带有丰富的标注的人脸活体检测据集 随着人脸识别应用的广泛部署，针对人脸识别系统的工具越来越多，人脸活体检测（反欺诈）成为重要的研究方向。 Download scientific diagram | Sample data from the CASIA dataset. ) and related resources. Our dataset comprises 853,729 images of 321,751 spoof subjects and SiW (Spoofing in the Wild) is a face anti-spoofing dataset recently introduced in [29] where images are extracted from short videos captured at high resolution and 30 frames per second. The authors observed that transfer Jun 28, 2021 · Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs). CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations. We proposes a novel two-stream CNN-based face antispoofing method, for print and replay attacks. The database contains 600 video recordings, in which 240 videos of 20 subjects are used for training and 360 videos of 30 subjects for testing. CelebA-Spoof is a large-scale face anti-spoofing dataset recently introduced in [53]. A curated list of Face Authentication Security (including face anti-spoofing/face presentation attack/face liveness detection, face attack models, etc. CelebA-Spoof：大规模带有丰富的标注的人脸活体检测据集. face anti-spoo ng datasets are limited in both quantity and diversity. Abstract: CelebA-Spoof is a large-scale face anti-spoofing dataset that has 625,537 images from 10,177 subjects, which includes 43 rich attributes on face, illumination,environment and spoof types. Among 43 rich attributes, 40 attributes belong to CelebA-Spoof is a large-scale face anti-spoofing dataset with the following properties: Quantity: CelebA-Spoof comprises of 625,537 pictures of 10,177 subjects, significantly larger than the existing datasets. We collect and annotate spoof images for CelebA-Spoof. It acts as an important step to select the face image to the face recognition system. Jul 24, 2020 · To overcome these obstacles, we contribute a large-scale face anti-spoofing dataset, CelebA-Spoof, with the following appealing properties: 1) Quantity: CelebA-Spoof comprises of 625,537 pictures of 10,177 subjects, significantly larger than the existing datasets. Another multi-modal face PAD dataset is Msspoof [9], containing visible (VIS) and near-infrared (NIR) images of real accesses and printed spoofing attacks with ≤21objects. 随着人脸识别应用的广泛部署，针对人脸识别系统的工具越来越多，人脸活体检测（反欺诈）成为重要的研究方向。 Oct 10, 2020 · 3 main points ️ Proposed "CelebA-Spoof", a large dataset for face impersonation prevention containing 43 rich attribute information ️ The multitasking framework AENet was used to examine the impact of attribute information on the task of preventing face spoofing. Therefore, we segregated all the RGB images from the dataset. However, this Feb 15, 2023 · The challenge will officially start together with 4th Face Anti-spoofing Workshop. In the spoof photos, there are three major categories and 17 subcategories. Source: Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing Dataset for face anti-spoofing in terms of both subjects and modalities. Live images are selected from the CelebA dataset. However, compared to the large existing image classification and face recognition datasets, face anti-spoofing datasets have less than 170 170 170 subjects and 60, 00 60 00 60,00 video clips, see Table 1. For masking attack, masks with and without cropping are considered. Face anti-spoofing is a method of combating the cheating of face recognition systems. 99,67% accuracy on our dataset and perfect scores on multiple public Jan 1, 2014 · The first public dataset for studying anti-spoofing in face recognition appeared in 2010, accompanying the work of Tan and others in []. ️Three generic benchmarks were proposed to support a comprehensive assessment. 6 images for each subject. It Aug 26, 2019 · Face anti-spoofing plays an important role in face recognition system to prevent security vulnerability. Previous works have provided many databases for face anti-spoofing, but they either contain too few subjects or contain a single modal. To address the limitations of existing face anti-spoofing datasets, we introduce the Wild Face Anti-Spoofing Dataset (WFAS), a large-scale FAS dataset collected in the wild. It covers hybrid (handcrafted+deep), pure deep learning, and generalized learning based methods for monocular RGB face anti-spoofing. Evaluation with a face spoof detection dataset demonstrated improved results over the existing the-state-of-the-art methods. With the emergence of large-scale academic datasets in the Aug 23, 2020 · The main reason is that current face anti-spoofing datasets are limited in both quantity and diversity. It also includes multi-modal learning based methods as well as specialized sensor Facial anti-spoofing is the task of preventing false facial verification by using a photo, video, mask or a different substitute for an authorized person’s face. pro/datasets to learn about the price and buy the dataset Content The dataset includes files folder with videos of people. We present a comprehensive review of recent deep learning methods for face anti-spoofing (mostly from 2018 to 2022). The image is printed or displayed on a digital device. Image Courtesy: []In this article, let’s take a deep dive into the world of face anti-spoofing. Among 43 rich attributes, 40 attributes belong to Introduction. As more and more realistic PAs with novel types spring up, traditional FAS methods based on handcrafted features become unreliable due to their limited representation capacity. Our competition encompasses over 800K spoof photos and over 500K live photos. 2) Diversity: The spoof images are captured from 8 scenes (2 environments * 4 CelebA-Spoof is a large-scale face anti-spoofing dataset that has 625,537 images from 10,177 subjects, which includes 43 rich attributes on face, illumination, environment and spoof types. In our experiment, we will use GANs to generate a unique synthetic (spoofing) dataset. The limited number of Jun 1, 2022 · What are the main databases for face anti-spoofing? Face anti-spoofing includes a number of notable databases used for training and testing. Oct 1, 2020 · The main reason is that current face anti-spoofing datasets are limited in both quantity and diversity. The characteristic of Large-scale and diversity can further fill the gap between face anti-spoofing dataset and real scenes. Despite substantial advancements, the generalization of existing approaches to real-world applications remains challenging. Aug 26, 2019 · The influences of different modalities on the performance of face anti-spoofing are studied by extensively experimenting with different combinations of modalities with an end-to-end deep learning model, and it is found that the HDR information contributes most among differentmodalities. In recent years, face anti-spoofing algorithms have seen great progress. While face anti-spoofing tech-niques have received much attention to aim at identifying whether the captured face is genuine or fake, most face-spoofing detection To address these shortcomings, we introduce the Wild Face Anti-Spoofing (WFAS) dataset, a large-scale, diverse FAS dataset collected in unconstrained settings. The first dataset FERET mostly focused on recognizing faces. - **Replay/video attack**: A more sophisticated way to trick the system, which usually The dataset includes a diverse range of face images, both genuine and spoofed, to train and evaluate the face anti-spoofing models. SiW-Mv2 dataset contains 14 spoof types spanning from typical print and replay attack, to various masks, impersonation makeup and physical material coverings. Face Anti-spoofing Methods. Some examples of attacks: - **Print attack**: The attacker uses someone’s photo. Two kinds of cameras with different resolutions (720×480 and 640×480) were used to record the videos from the 35 individuals. Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Live image selected from the CelebA dataset. Three different spoof attacks are designed: replay, warp print and cut print attacks. In total, 4,478 videos are collected from 165 subjects including variations in spoof type, recording device, illumination condition, pose and facial expression. To overcome these obstacles, we contribute a large-scale face anti-spoofing dataset, CelebA The study about the vulnerabilities of biometric systems against spoofing has been a very active field of research in recent years. All datasets mentioned above are listed in Table 1. Biometric Attack Dataset The dataset is created on the basis of Anti Spoofing Real Dataset. Jul 24, 2020 · A large-scale multi-modal dataset, namely CASIA-SURF, is introduced, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and visual modalities and a new multi- modal fusion method is presented, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modal. As a result, conventional face recognition systems can be very vulnerable to such PAs. This CelebA-Spoof 71 Keywords: Face anti-spoofing · Large-scale dataset 1 Introduction Face anti-spoofing is an important task in computer vision, which aims to facil-itate facial interaction systems to determine whether a presented face is live or spoof. We have noticed that currently most of face replay anti-spoofing databases focus on data with little variations of the devices used for replay and record. . Potentially could be used in security systems, biometrics, attendence systems and etc. Aug 23, 2019 · As can be known from the above, these datasets contain fewer subjects and most datasets only include RGB images. However, existing datasets in the face PAD community have two common limitations. SiW-Mv2 has the largest variance in terms of the spoof pattern, each of these patterns are designated and verified by the IARPA project. , 2013). The dataset was collected with three goals in mind: (i) to have both a large number of identities and also a Face anti-spoofing (FAS) plays a vital role in preventing face recognition (FR) systems from presentation attacks. The MSU-MFSD dataset contains 280 video recordings of genuine and attack faces. 💴 Buy the Dataset: This is just an example of the data. CelebA-Spoof: Large-Scale Face Anti-Spoofing The dataset contains 3. For the real accesses, each individual has two video recordings captured with the Laptop To address these shortcomings in existing face anti-spoofing dataset, in this work we propose a large-scale and densely annotated dataset, CelebA-Spoof. To ensure accurate liveness detection and anti-spoofing in face recognition systems, a cavalcade of datasets has been introduced. CASIA-FASD is a small face anti-spoofing dataset containing 50 subjects. Annotation Richness: CelebA-Spoof contains 10 spoof type Please cite the following papers in your publications if it helps your research: @article{zhang2020casia, title={Casia-surf: A large-scale multi-modal benchmark for face anti-spoofing}, author={Zhang, Shifeng and Liu, Ajian and Wan, Jun and Liang, Yanyan and Guo, Guodong and Escalera, Sergio and Escalante, Hugo Jair and Li, Stan Z}, journal={IEEE Transactions on Biometrics, Behavior, and CASIA-MFSD is a dataset for face anti-spoofing. It contains 50 subjects, and 12 videos for each subject under different resolutions and light conditions. In this Aug 13, 2020 · CelebA-Spoof is an anti-spoofing dataset that consists of 625,537 images of 10,177 people. This dataset has subjects with modalities RGB, depth, and IR; however, in our approach, we will only be using the RGB images from this dataset. A single image is needed to compute liveness score. The dataset contains 625,537 images of 10,177 celebrities captured under different spoof mediums, environments and illumination conditions. Besides the standard Spoof Type annotation, CelebA-Spoof also contains annotations for Illumination Condition and Environment, which express more information in face anti-spoofing, compared to categorical label like Live/Spoof. Facial anti-spoofing is the task of preventing false facial verification by using a photo, video, mask or a different substitute for an authorized person’s face. Leave a request on https://trainingdata. The subsequent Since face is the most accessible biometric modality, there have been many different types of PAs for faces including print attack, replay attack, 3D masks, etc. We collect a comprehensive Near-Infrared face anti-spoofing Dataset, which incorporates six illumination conditions and comprises 380,000 images from 1,040 distinct identities. Large-scale and diverse datasets are pivotal for deep learning based methods during both training and evaluating phases. We suggest you the dataset similar to CelebA Dataset but with photos of real people, additionally the dataset for face anti spoofing and face recognition includes not only images, but videos of the individuals! Face detection/recognition has been the most popular deep learning projects/researches for these past years. These datasets have promoted the development of face anti-spoofing technology and many researchers have further improved the performance of face anti-spoofing using datasets published at an earlier time. Therefore, face-spoofing de-tection has become a critical requirements for any face recognition system to filter out fake faces [29]. Images are downloaded from Google Image Search and have large variations in pose, age, illumination, ethnicity and profession (e. In this work, the authors explore the Lambertian reflectance model to derive differences between the 2D images of the face presented during an attack and a real (3D) face, in real-access attempts. This limitation can be attributed to the scarcity and lack of diversity in publicly available FAS datasets, which often leads to overfitting during Jul 17, 2024 · To address the limitations of existing Near-Infrared Dataset in the diversity of identities acquisition environment and acquisition devices. Registration is now open on codalab. Jul 17, 2024 · To address the limitations of existing Near-Infrared Dataset in the diversity of identities acquisition environment and acquisition devices. After generating the synthetic dataset, we explore spoofing mitigation and propose a HOG + Patch-based CNN architecture. In this particular research we are focusing on one of the most difficult types of attack - video replay. Face Anti-Spoofing Using Patch and Depth-Based CNNs, 2017 [9] Yaojie Liu, Amin Jourabloo, Xiaoming Liu, Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision ，CVPR2018 [10] Discriminative Representation Combinations for Accurate Face Spoofing Detection ”，2018 PR，上海交大 [11] Face De-Spoofing: Anti-Spoofing Jun 21, 2024 · In this work, we propose generating a novel face spoofing dataset with the DCGAN framework. , RGB, Depth and IR). Specifically, it consists of subjects with videos and each sample has modalities (i. 35 individuals have participated in the development of this database with a total of 280 videos. 2. The study about the vulnerabilities of biometric systems against spoofing has been a very active field of research in recent years. Nagpal and Dubey carried out extensive experiments using different pre-trained models to detect spoofed faces. actors, athletes, politicians). The original dataset proposes three different evaluation protocols. Nowadays, FAS systems face the challenge of domain shift, impacting the generalization performance of existing FAS methods. Mar 16, 2024 · One of the key points of this success is the availability of face anti-spoofing datasets [5, 7, 10, 32, 48, 53]. Diversity: The spoof images are captured from 8 scenes (2 environments * 4 illumination conditions) with more than 10 sensors. Aug 15, 2023 · Currently, face recognition technology (FRT) has been applied ubiquitously. File with the extension . g. Jun 6, 2023 · Figure 1: FAS pipeline in face recognition systems with different spoofing layer schemes. csv id: id of the person, file: link to access the display spoof attack video Apr 28, 2024 · CASIA-SURF is a large-scale multi-modal benchmark for face anti-spoofing. Apr 1, 2024 · This paper presents a new algorithm for detecting face spoofing attacks, with a focus on replay and print attacks. However, due to the abuse of personal face photos on social media, FRT has encountered unprecedented challenges which promote the development of face spoofing detection (also called face liveness detection or face anti-spoofing) technology. 1 Generating Spoofing Dataset. With rich annotations we can better analyze face anti-spoofing task. RGB modalities. One of its daily application is the face verification feature to perform tasks on our devices (e. wjhn tcxhlj sbjy dcdr oaxk less lrcs hhcwl kolb susrdy