Synthesizing realistic profile faces is beneficial for more efficiently training deep pose-invariant models for large-scale unconstrained face recognition, by augmenting the number of samples with ...extreme poses and avoiding costly annotation work. However, learning from synthetic faces may not achieve the desired performance due to the discrepancy betwedistributions of the synthetic and real face images. To narrow this gap, we propose a Dual-Agent Generative Adversarial Network (DA-GAN) model, which can improve the realism of a face simulator's output using unlabeled real faces while preserving the identity information during the realism refinement. The dual agents are specially designed for distinguishing real versus fake and identities simultaneously. In particular, we employ an off-the-shelf 3D face model as a simulator to generate profile face images with varying poses. DA-GAN leverages a fully convolutional network as the generator to generate high-resolution images and an auto-encoder as the discriminator with the dual agents. Besides the novel architecture, we make several key modifications to the standard GAN to preserve pose, texture as well as identity, and stabilize the training process: (i) a pose perception loss; (ii) an identity perception loss; (iii) an adversarial loss with a boundary equilibrium regularization term. Experimental results show that DA-GAN not only achieves outstanding perceptual results but also significantly outperforms state-of-the-arts on the large-scale and challenging NIST IJB-A and CFP unconstrained face recognition benchmarks. In addition, the proposed DA-GAN is also a promising new approach for solving generic transfer learning problems more effectively. DA-GAN is the foundation of our winning entry to the NIST IJB-A face recognition competition in which we secured the 1^{st} places on the tracks of verification and identification.
In a world where security issues have been gaining growing importance, face recognition systems have attracted increasing attention in multiple application areas, ranging from forensics and ...surveillance to commerce and entertainment. To help to understand the landscape and abstraction levels relevant for face recognition systems, face recognition taxonomies allow a deeper dissection and comparison of the existing solutions. This study proposes a new, more encompassing and richer multi-level face recognition taxonomy, facilitating the organisation and categorisation of available and emerging face recognition solutions; this taxonomy may also guide researchers in the development of more efficient face recognition solutions. The proposed multi-level taxonomy considers levels related to the face structure, feature support, and feature extraction approach. Following the proposed taxonomy, a comprehensive survey of representative face recognition solutions is presented. This study concludes with a discussion on current algorithmic and application related challenges, which may define future research directions for face recognition.
Existing domain generalization methods for face anti-spoofing endeavor to extract common differentiation features to improve the generalization. However, due to large distribution discrepancies among ...fake faces of different domains, it is difficult to seek a compact and generalized feature space for the fake faces. In this work, we propose an end-to-end single-side domain generalization framework (SSDG) to improve the generalization ability of face anti-spoofing. The main idea is to learn a generalized feature space, where the feature distribution of the real faces is compact while that of the fake ones is dispersed among domains but compact within each domain. Specifically, a feature generator is trained to make only the real faces from different domains undistinguishable, but not for the fake ones, thus forming a single-side adversarial learning. Moreover, an asymmetric triplet loss is designed to constrain the fake faces of different domains separated while the real ones aggregated. The above two points are integrated into a unified framework in an end-to-end training manner, resulting in a more generalized class boundary, especially good for samples from novel domains. Feature and weight normalization is incorporated to further improve the generalization ability. Extensive experiments show that our proposed approach is effective and outperforms the state-of-the-art methods on four public databases. The code is released online.
This paper introduces a method for face recognition across age and also a dataset containing variations of age in the wild. We use a data-driven method to address the cross-age face recognition ...problem, called cross-age reference coding (CARC). By leveraging a large-scale image dataset freely available on the Internet as a reference set, CARC can encode the low-level feature of a face image with an age-invariant reference space. In the retrieval phase, our method only requires a linear projection to encode the feature and thus it is highly scalable. To evaluate our method, we introduce a large-scale dataset called cross-age celebrity dataset (CACD). The dataset contains more than 160 000 images of 2,000 celebrities with age ranging from 16 to 62. Experimental results show that our method can achieve state-of-the-art performance on both CACD and the other widely used dataset for face recognition across age. To understand the difficulties of face recognition across age, we further construct a verification subset from the CACD called CACD-VS and conduct human evaluation using Amazon Mechanical Turk. CACD-VS contains 2,000 positive pairs and 2,000 negative pairs and is carefully annotated by checking both the associated image and web contents. Our experiments show that although state-of-the-art methods can achieve competitive performance compared to average human performance, majority votes of several humans can achieve much higher performance on this task. The gap between machine and human would imply possible directions for further improvement of cross-age face recognition in the future.
Masked Face Recognition Dataset and Application Wang, Zhongyuan; Huang, Baojin; Wang, Guangcheng ...
IEEE transactions on biometrics, behavior, and identity science,
2023-April, 2023-4-00, Letnik:
5, Številka:
2
Journal Article
Recenzirano
Odprti dostop
During COVID-19 coronavirus epidemic, almost everyone wears a mask to prevent the spread of virus. It raises a problem that the traditional face recognition model basically fails in the scene of ...face-based identity verification, such as security check, community visit check-in, etc. Therefore, it is imminent to boost the performance of masked face recognition. Most recent advanced face recognition methods are based on deep learning, which heavily depends on a large number of training samples. However, there are presently no publicly available masked face recognition datasets, especially real ones. To this end, this work proposes three types of masked face datasets, including Masked Face Detection Dataset (MFDD), Real-world Masked Face Recognition Dataset (RMFRD) and Synthetic Masked Face Recognition Dataset (SMFRD). Besides, we conduct benchmark experiments on these three datasets for reference. As far as we know, we are the first to publicly release large-scale masked face recognition datasets that can be downloaded for free at https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset .
•A novel nonlinear coupled mapping architecture using two deep convolutional neural networks.•Achieves high recognition accuracy especially when the probe image is extremely low resolution.•With the ...embedded super-resolution CNN, it reconstructs high resolution version of the input image.
We propose a novel coupled mappings method for low resolution face recognition using deep convolutional neural networks (DCNNs). The proposed architecture consists of two branches of DCNNs to map the high and low resolution face images into a common space with nonlinear transformations. The branch corresponding to transformation of high resolution images consists of 14 layers and the other branch which maps the low resolution face images to the common space includes a 5-layer super-resolution network connected to a 14-layer network. The distance between the features of corresponding high and low resolution images are backpropagated to train the networks. Our proposed method is evaluated on FERET, LFW, and MBGC datasets and compared with state-of-the-art competing methods. Our extensive experimental evaluations show that the proposed method significantly improves the recognition performance especially for very low resolution probe face images (5% improvement in recognition accuracy). Furthermore, it can reconstruct a high resolution image from its corresponding low resolution probe image which is comparable with the state-of-the-art super-resolution methods in terms of visual quality.
With the recent COVID-19 pandemic, wearing masks has become a necessity in our daily lives. People are encouraged to wear masks to protect themselves from the outside world and thus from infection ...with COVID-19. The presence of masks raised serious concerns about the accuracy of existing facial recognition systems since most of the facial features are obscured by the mask. To address these challenges, a new method for masked face recognition is proposed that combines a cropping-based approach (upper half of the face) with an improved VGG-16 architecture. The finest features from the un-occluded facial region are extracted using a transfer learned VGG-16 model (Forehead and eyes). The optimal cropping ratio is investigated to give an enhanced feature representation for recognition. To avoid the overhead of bias, the obtained feature vector is mapped into a lower-dimensional feature representation using a Random Fourier Feature extraction module. Comprehensive experiments on the Georgia Tech Face Dataset, Head Pose Image Dataset, and Face Dataset by Robotics Lab show that the proposed approach outperforms other state-of-the-art approaches for masked face recognition.
Near infrared-visible (NIR-VIS) heterogeneous face recognition refers to the process of matching NIR to VIS face images. Current heterogeneous methods try to extend VIS face recognition methods to ...the NIR spectrum by synthesizing VIS images from NIR images. However, due to the self-occlusion and sensing gap, NIR face images lose some visible lighting contents so that they are always incomplete compared to VIS face images. This paper models high-resolution heterogeneous face synthesis as a complementary combination of two components: a texture inpainting component and a pose correction component. The inpainting component synthesizes and inpaints VIS image textures from NIR image textures. The correction component maps any pose in NIR images to a frontal pose in VIS images, resulting in paired NIR and VIS textures. A warping procedure is developed to integrate the two components into an end-to-end deep network. A fine-grained discriminator and a wavelet-based discriminator are designed to improve visual quality. A novel 3D-based pose correction loss, two adversarial losses, and a pixel loss are imposed to ensure synthesis results. We demonstrate that by attaching the correction component, we can simplify heterogeneous face synthesis from one-to-many unpaired image translation to one-to-one paired image translation, and minimize the spectral and pose discrepancy during heterogeneous recognition. Extensive experimental results show that our network not only generates high-resolution VIS face images but also facilitates the accuracy improvement of heterogeneous face recognition.
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