A close relationship exists between the advancement of face recognition algorithms and the availability of face databases varying factors that affect facial appearance in a controlled manner. The CMU ...PIE database has been very influential in advancing research in face recognition across pose and illumination. Despite its success the PIE database has several shortcomings: a limited number of subjects, a single recording session and only few expressions captured. To address these issues we collected the CMU Multi-PIE database. It contains 337 subjects, imaged under 15 view points and 19 illumination conditions in up to four recording sessions. In this paper we introduce the database and describe the recording procedure. We furthermore present results from baseline experiments using PCA and LDA classifiers to highlight similarities and differences between PIE and Multi-PIE.
3D face recognition: a survey Zhou, Song; Xiao, Sheng
Human-centric computing and information sciences,
11/2018, Letnik:
8, Številka:
1
Journal Article
Recenzirano
Odprti dostop
3D face recognition has become a trending research direction in both industry and academia. It inherits advantages from traditional 2D face recognition, such as the natural recognition process and a ...wide range of applications. Moreover, 3D face recognition systems could accurately recognize human faces even under dim lights and with variant facial positions and expressions, in such conditions 2D face recognition systems would have immense difficulty to operate. This paper summarizes the history and the most recent progresses in 3D face recognition research domain. The frontier research results are introduced in three categories: pose-invariant recognition, expression-invariant recognition, and occlusion-invariant recognition. To promote future research, this paper collects information about publicly available 3D face databases. This paper also lists important open problems.
•An up-to-date, comprehensive and compact overview of the vast amount of work on image and video based face recognition in the literature.•A novel taxonomy of image and video-based methods, which ...also contains recent methods such as sparsity and deep learning based methods.•An up-to-date review of the image and video-based data sets used for face recognition.•Review of the recent deep-learning based methods, which have shown remarkable results on large scale and unconstrained challenging data sets.•Information on both image and video-based methods, with an emphasis on the video-based methods.
Biometric systems have the goal of measuring and analyzing the unique physical or behavioral characteristics of an individual. The main feature of biometric systems is the use of bodily structures with distinctive characteristics. In the literature, there are biometric systems that use physiological features (fingerprint, iris, palm print, face, etc.) as well as systems that use behavioral characteristics (signature, walking, speech patterns, facial dynamics, etc.) Recently, facial biometrics has been one of the most preferred biometric data since it generally does not require the cooperation of the user and can be obtained without violating the personal private space. In this paper, the methods used to obtain and classify facial biometric data in the literature have been summarized. We give a taxonomy of image-based and video-based face recognition methods, outline the major historical developments, and the main processing steps. Popular data sets that have been used for face recognition by researchers are also reviewed. We also cover the recent deep-learning based methods for face recognition and point out possible directions for future research.
Deep learning applies multiple processing layers to learn representations of data with multiple levels of feature extraction. This emerging technique has reshaped the research landscape of face ...recognition (FR) since 2014, launched by the breakthroughs of DeepFace and DeepID. Since then, deep learning technique, characterized by the hierarchical architecture to stitch together pixels into invariant face representation, has dramatically improved the state-of-the-art performance and fostered successful real-world applications. In this survey, we provide a comprehensive review of the recent developments on deep FR, covering broad topics on algorithm designs, databases, protocols, and application scenes. First, we summarize different network architectures and loss functions proposed in the rapid evolution of the deep FR methods. Second, the related face processing methods are categorized into two classes: “one-to-many augmentation” and “many-to-one normalization”. Then, we summarize and compare the commonly used databases for both model training and evaluation. Third, we review miscellaneous scenes in deep FR, such as cross-factor, heterogenous, multiple-media and industrial scenes. Finally, the technical challenges and several promising directions are highlighted.
WebFace260M: A Benchmark for Million-Scale Deep Face Recognition Zhu, Zheng; Huang, Guan; Deng, Jiankang ...
IEEE transactions on pattern analysis and machine intelligence,
2023-Feb.-1, 2023-02-00, 2023-2-1, 20230201, Letnik:
45, Številka:
2
Journal Article
Recenzirano
Odprti dostop
Face benchmarks empower the research community to train and evaluate high-performance face recognition systems. In this paper, we contribute a new million-scale recognition benchmark, containing ...uncurated 4M identities/260M faces (WebFace260M) and cleaned 2M identities/42M faces (WebFace42M) training data, as well as an elaborately designed time-constrained evaluation protocol. First, we collect 4M name lists and download 260M faces from the Internet. Then, a Cleaning Automatically utilizing Self-Training (CAST) pipeline is devised to purify the tremendous WebFace260M, which is efficient and scalable. To the best of our knowledge, the cleaned WebFace42M is the largest public face recognition training set and we expect to close the data gap between academia and industry. Referring to practical deployments, Face Recognition Under Inference Time conStraint (FRUITS) protocol and a new test set with rich attributes are constructed. Besides, we gather a large-scale masked face sub-set for biometrics assessment under COVID-19. For a comprehensive evaluation of face matchers, three recognition tasks are performed under standard, masked and unbiased settings, respectively. Equipped with this benchmark, we delve into million-scale face recognition problems. A distributed framework is developed to train face recognition models efficiently without tampering with the performance. Enabled by WebFace42M, we reduce 40% failure rate on the challenging IJB-C set and rank 3rd among 430 entries on NIST-FRVT. Even 10% data (WebFace4M) shows superior performance compared with the public training sets. Furthermore, comprehensive baselines are established under the FRUITS-100/500/1000 milliseconds protocols. The proposed benchmark shows enormous potential on standard, masked and unbiased face recognition scenarios. Our WebFace260M website is https://www.face-benchmark.org .
Sparse representation has shown an attractive performance in a number of applications. However, the available sparse representation methods still suffer from some problems, and it is necessary to ...design more efficient methods. Particularly, to design a computationally inexpensive, easily solvable, and robust sparse representation method is a significant task. In this paper, we explore the issue of designing the simple, robust, and powerfully efficient sparse representation methods for image classification. The contributions of this paper are as follows. First, a novel discriminative sparse representation method is proposed and its noticeable performance in image classification is demonstrated by the experimental results. More importantly, the proposed method outperforms the existing state-of-the-art sparse representation methods. Second, the proposed method is not only very computationally efficient but also has an intuitive and easily understandable idea. It exploits a simple algorithm to obtain a closed-form solution and discriminative representation of the test sample. Third, the feasibility, computational efficiency, and remarkable classification accuracy of the proposed l₂ regularization-based representation are comprehensively shown by extensive experiments and analysis. The code of the proposed method is available at http://www.yongxu.org/lunwen.html.
The large domain discrepancy between faces captured in polarimetric (or conventional) thermal and visible domains makes cross-domain face verification a highly challenging problem for human examiners ...as well as computer vision algorithms. Previous approaches utilize either a two-step procedure (visible feature estimation and visible image reconstruction) or an input-level fusion technique, where different Stokes images are concatenated and used as a multi-channel input to synthesize the visible image given the corresponding polarimetric signatures. Although these methods have yielded improvements, we argue that input-level fusion alone may not be sufficient to realize the full potential of the available Stokes images. We propose a generative adversarial networks based multi-stream feature-level fusion technique to synthesize high-quality visible images from polarimetric thermal images. The proposed network consists of a generator sub-network, constructed using an encoder–decoder network based on dense residual blocks, and a multi-scale discriminator sub-network. The generator network is trained by optimizing an adversarial loss in addition to a perceptual loss and an identity preserving loss to enable photo realistic generation of visible images while preserving discriminative characteristics. An extended dataset consisting of polarimetric thermal facial signatures of 111 subjects is also introduced. Multiple experiments evaluated on different experimental protocols demonstrate that the proposed method achieves state-of-the-art performance. Code will be made available at
https://github.com/hezhangsprinter
.
Face images captured in unconstrained environments usually contain significant pose variation, which dramatically degrades the performance of algorithms designed to recognize frontal faces. This ...paper proposes a novel face identification framework capable of handling the full range of pose variations within ±90° of yaw. The proposed framework first transforms the original pose-invariant face recognition problem into a partial frontal face recognition problem. A robust patch-based face representation scheme is then developed to represent the synthesized partial frontal faces. For each patch, a transformation dictionary is learnt under the proposed multi-task learning scheme. The transformation dictionary transforms the features of different poses into a discriminative subspace. Finally, face matching is performed at patch level rather than at the holistic level. Extensive and systematic experimentation on FERET, CMU-PIE, and Multi-PIE databases shows that the proposed method consistently outperforms single-task-based baselines as well as state-of-the-art methods for the pose problem. We further extend the proposed algorithm for the unconstrained face verification problem and achieve top-level performance on the challenging LFW data set.
The field of face recognition has recently become a quite popular area of research, which is of great significance to the development of technology. It introduced the definition of face recognition ...technology and its development process, as well as the technical advantages and application scenarios of the technology. with the development of deep learning, face recognition technology based on deep learning is gradually realized. firstly, the limitations of traditional face recognition technology are pointed out. Then, several popular face recognition methods based on depth learning are analyzed. Finally, this paper introduces the application of deep learning technology in face recognition, summarizes a new deep learning model based on big data, and summarizes and prospects the development of face recognition technology in the future.
Recently, a popular line of research in face recognition is adopting margins in the well-established softmax loss function to maximize class separability. In this paper, we first introduce an ...Additive Angular Margin Loss (ArcFace), which not only has a clear geometric interpretation but also significantly enhances the discriminative power. Since ArcFace is susceptible to the massive label noise, we further propose sub-center ArcFace, in which each class contains <inline-formula><tex-math notation="LaTeX">K</tex-math> <mml:math><mml:mi>K</mml:mi></mml:math><inline-graphic xlink:href="deng-ieq1-3087709.gif"/> </inline-formula> sub-centers and training samples only need to be close to any of the <inline-formula><tex-math notation="LaTeX">K</tex-math> <mml:math><mml:mi>K</mml:mi></mml:math><inline-graphic xlink:href="deng-ieq2-3087709.gif"/> </inline-formula> positive sub-centers. Sub-center ArcFace encourages one dominant sub-class that contains the majority of clean faces and non-dominant sub-classes that include hard or noisy faces. Based on this self-propelled isolation, we boost the performance through automatically purifying raw web faces under massive real-world noise. Besides discriminative feature embedding, we also explore the inverse problem, mapping feature vectors to face images. Without training any additional generator or discriminator, the pre-trained ArcFace model can generate identity-preserved face images for both subjects inside and outside the training data only by using the network gradient and Batch Normalization (BN) priors. Extensive experiments demonstrate that ArcFace can enhance the discriminative feature embedding as well as strengthen the generative face synthesis.
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