Face Segmentation, Face Swapping, and How They Impact Face Recognition

Secure Learning using Face Recognition has gained significant attention due to its potential solution in curbing and elimination of electronic examination impersonation through enrollment, authentication and authorization. Despite the advancements, existing system often focuses on individual entities, void of models and special dataset to enhance security measure in comprehensively curbing electronic examination impersonation. This study aims to address this gap by performing a model using face recognition: Labeled Face in the Wild (LFW), Custom Dataset, algorithms using TensorFlowJs and Face Application Programming Interface (API) model. The research evaluates this model on a unified facial recognition dataset, assessing their performance based on face verification and recognition. The LFW dataset a popular benchmark dataset for face verification and recognition focuses on detecting and alignment of captured images, where TensorFlowJs extract face embedding, while the Face API verifies captured image by loading the Custom Dataset (Enrolled images) comparing with that of the recent before successful authentication. The LFW-TensorFlowJs combines these approaches to leverage both Secure and accurate information. Results shows that integrating LFW, TensorFlowJs and Face API model outperforms the standalone LFW.

Face-swapping technology has been widely used in people's life, and people also put forward higher requirements for it. Most of the current face-swapping methods are difficult to generate a high-definition face image. Through StyleGAN, we can generate high-definition face images. However, face-swapping with StyleGAN is still challenging. Firstly, we need to map the target image to the latent space of StyleGAN. Many tasks need to map the input image to a new latent space for face-swapping, because identity features are complex and challenging to map to specific latent space layers directly. So face-swapping is completed in the remapping process, which consumes excess computing resources for reconstruction. And the generated image is difficult to maintain the original image color, face attributes, background and other attributes. We propose a new method, which only edits the code of w+ latent space of StyleGAN to complete the face-swapping and generate high-definition face images. We propose the GAN inversion method to improve the effect of face swapping, which combines convolution networks' advantages in extracting texture features and the benefits of transformers in extracting structure features. In the latent space of StyleGAN, the low-level feature layer is dominated by structure information, and the high-level feature layer is overwhelmed by texture information. Furthermore, we propose latent space selection, through which the neural network can learn disentangled representations of identity information in the latent space. Finally, we improved the post-processing process of face swapping to keep the image's background. Our method can complete face-swapping by editing the w+ space. Thus, high-quality face image can be generated and a lot of computing resource is saved on image reconstruction. At the same time, our method can keep other attributes better in the face-swapping process.

Application of Face Recognition Method Under Deep Learning Algorithm in Embedded Systems

In this paper, we present an integrated system for automatically generating and editing face images through face swapping, attribute-based editing, and random face parts synthesis. The proposed system is based on a deep neural network that variationally learns the face and hair regions with large-scale face image datasets. Different from conventional variational methods, the proposed network represents the latent spaces individually for faces and hairs. We refer to the proposed network as region-separative generative adversarial network (RSGAN). The proposed network independently handles face and hair appearances in the latent spaces, and then, face swapping is achieved by replacing the latent-space representations of the faces, and reconstruct the entire face image with them. This approach in the latent space robustly performs face swapping even for images which the previous methods result in failure due to inappropriate fitting or the 3D morphable models. In addition, the proposed system can further edit face-swapped images with the same network by manipulating visual attributes or by composing them with randomly generated face or hair parts.

Face swapping aims to transfer the identity of a source face to a target face image while preserving the target attributes (e.g., facial expression, head pose, illumination, and background). Most existing methods use a face recognition model to extract global features from the source face and directly fuse them with the target to generate a swapping result. However, identity-irrelevant attributes (e.g., hairstyle and facial appearances) contribute a lot to the recognition task, and thus swapping this task-specific feature inevitably interfuses source attributes with target ones. In this paper, we propose an identity-aware variational autoencoder (ID-VAE) based face swapping framework, dubbed VAFSwap, which learns disentangled identity and attribute representations for high-fidelity face swapping. In particular, we overcome the unpaired training barrier of VAE and impose a proxy identity on the latent space by exploiting the weak supervision from an auxiliary image set whose identity is averaged from multiple collected face images. To explicitly guide the identity fusion, we further devise an identity-associated matrix that corresponds different face regions with their identity representations to perform identity-related feature interactions. Finally, we incorporate spatial dimensions into the latent space and exploit the generative priors of a pre-trained face generator, allowing the effective elimination of noticeable swapping artifacts. Extensive experiments on the FaceForensics++ and CelebA-HQ datasets demonstrate that our method outperforms the state-of-the-art significantly.

In this study, we revisit the fundamental setting of face-swapping models and reveal that only using implicit supervision for training leads to the difficulty of advanced methods to preserve the source identity. We propose a novel reverse pseudo-input generation approach to offer supplemental data for training face-swapping models, which addresses the aforementioned issue. Unlike the traditional pseudo-label-based training strategy, we assume that arbitrary real facial images could serve as the ground-truth outputs for the face-swapping network and try to generate corresponding input <source, target> pair data. Specifically, we involve a source-creating surrogate that alters the attributes of the real image while keeping the identity, and a target-creating surrogate intends to synthesize attribute-preserved target images with different identities. Our framework, which utilizes proxy-paired data as explicit supervision to direct the face-swapping training process, partially fulfills a credible and effective optimization direction to boost the identity-preserving capability. We design explicit and implicit adaption strategies to better approximate the explicit supervision for face swapping. Quantitative and qualitative experiments on FF++, FFHQ, and wild images show that our framework could improve the performance of various face-swapping pipelines in terms of visual fidelity and ID preserving. Furthermore, we display applications with our method on re-aging, swappable attribute customization, cross-domain, and video face swapping. Code is available under https://github.com/ ICTMCG/CSCS.

In recent years, face manipulation has attracted much attention which has both positive and negative effects for us. Face swapping is a face manipulation technique that modifies the identity information while preserving the attribute information of the face. It is necessary for us to know the advanced methods for high-quality face swapping and generate high-quality face swapping images to train the forgery detection algorithm. Currently, the face swapping algorithm faces many challenges, such as poor quality of the generated images or poor generalization of the algorithm, and sometimes the attribute features of the source image are also transferred to the target identity, resulting in the loss of attribute information of the generated images. In this paper, we propose a GAN-based face swapping framework which can obtain high-fidelity face swapping images. Firstly, this framework uses two separate encoders to decouple the identity and attribute feature of face images. Then a feature fusion module is used to coupled the identity and attribute feature of the face. Specifically, we treat the face swapping problem as a style transfer problem to solve the feature fusion problem, furthermore, an attention mechanism is introduced to guide the feature fusion process. The experimental results show that our method is effective.

In face recognition, face rotation alignment is an important part of the recognition process. In this paper, we present a hierarchical detector system using eye and eye-pair detectors combined with a geometrical method for calculating the in-plane angle of a face image. Two feature extraction methods, the restricted Boltzmann machine and the histogram of oriented gradients, are compared to extract feature vectors from a sliding window. Then a support vector machine is used to accurately localize the eyes. After the eye coordinates are obtained through our eye detector, the in-plane angle is estimated by calculating the arc-tangent of horizontal and vertical parts of the distance between left and right eye center points. By using this calculated in-plane angle, the face is subsequently rotationally aligned. We tested our approach on three different face datasets: IMM, Labeled Faces in the Wild (LFW) and FERET. Moreover, to compare the effect of rotational aligning on face recognition performance, we performed experiments using a face recognition method using rotationally aligned and non-aligned face images from the IMM dataset. The results show that our method calculates the in-plane rotation angle with high precision and this leads to a significant gain in face recognition performance.

Recently, centre loss that aiming to assist Softmax loss with the objectives of both inter‐class dispension and intra‐class compactness simultaneously, has achieved remarkable performance on convolutional neural network‐based face recognition. However, its advantages highly rely on the centre feature assumption, which influences the capacity of the final obtained face features. Inspired by the centre loss approach, a novel Orientation Truncated Centre Learning is proposed, which takes advantage of an orientation truncated centre function to make the centre feature learning have more suitable orientation for deep face recognition. Three metrics are proposed to evaluate how discriminative are the distributions of the learned features for MNIST visualisation. Experimental results on several challenging benchmarks, including fine‐grained labelled faces in the wild (FGLFW), labelled faces in the wild (LFW), YouTube faces (YTF), and benchmark of large‐scale unconstrained face recognition (BLUFR), show that the proposed approach can easily generate more favourable results than several state‐of‐the‐art competitors.

Gender recognition is a challenging and innovative research topic in the present sophisticated world of visual technology. This study proposes a system which can identify the gender based on face image. For finding the location of the face region, each input image is divided into overlapping blocks and Gabor features are extracted with different scale and orientations. Generate the enhanced feature, concatenate mean, standard deviation and skewness of Gabor features which are obtained from each block. For detecting face region, this feature is passed to ensemble classifier. To recognise the gender, reinforced local binary patterns are used to extract the facial local features. Adaboost algorithm is used to select and classify the discriminative features such as male or female. The authors’ experimental results on Labeled Faces in the Wild (LFW), FERET and Gallagher databases for face detection using Gabor features achieve 98, 98.5 and 96.5% accuracy, respectively. Moreover, the reinforced local binary patterns achieve the accuracy for gender classification as 97.08, 98.5 and 94.21% on the LFW, FERET and Gallagher databases, respectively. Both are achieving improved performance compared with other standard methodologies described in the literature.

Face swapping is a technique that replaces a face in a target media with another face of a different identity from a source face image. Currently, research on the effective utilisation of prior knowledge and semantic guidance for photo‐realistic face swapping remains limited, despite the impressive synthesis quality achieved by recent generative models. In this paper, we propose a novel conditional Denoising Diffusion Probabilistic Model (DDPM) enforced by a two‐level face prior guidance. Specifically, it includes (i) an image‐level condition generated by a 3D Morphable Model (3DMM), and (ii) a high‐semantic level guidance driven by information extracted from several pre‐trained attribute classifiers, for high‐quality face image synthesis. Although swapped face image from 3DMM does not achieve photo‐realistic quality on its own, it provides a strong image‐level prior, in parallel with high‐level face semantics, to guide the DDPM for high fidelity image generation. The experimental results demonstrate that our method outperforms state‐of‐the‐art face swapping methods on benchmark datasets in terms of its synthesis quality, and capability to preserve the target face attributes and swap the source face identity.

Face recognition can be installed in a surveillance system so that it can be used for monitoring, tracking and access control. An excellent, intelligent surveillance system should be sensitive to the objects far away from the camera. Unfortunately, due to the long-distance, objects like human faces captured by the camera are too small to identify. As to enhance the subtle color differences in the face image, in this paper we first improve the resolution of the captured image using deep convolution neural networks (DCNNs). Then the efficient features are extracted and used to do classification. As for verifying the effectiveness of the proposed method, we used three databases including AR face database, Georgia Tech face database (GT) database, and Labelled Faces in the Wild (LFW) database, altogether, to conduct the training and testing. Compared to the existing approaches, experimental results show that the identification accuracy of the proposed method outperforms any existing approaches.

Face has been used as one of the mainstream manners for user identification. However, with the popularity of face-swapping apps, it takes only a few seconds to change the faces between two facial images. Such swapped results, when using improperly or carelessly, might create some security issues in certain applications. This paper is the first work to address the importance of this issue and discusses the feasibility to achieve an automated face swapping detection through machine learning. Several approaches are tested on a face swapping database derived from a face benchmarking repository. The best solution in the experiments achieved a detection accuracy of over 92%.

Face Recognition (FR) problem is one of the significant fields in computer vision. FR is used to identify the faces that appear over distributed cameras over the network. The problem of face recognition can be divided into two categories, the first is recognition with more than one sample per person, which can be called traditional face recognition problem. The second is the recognition of faces using only a Single Sample Per Person (SSPP). The efficiency of face recognition systems decreases because of limited references especially (SSPP) and faces taken in the Operational Domain (OD) different from faces in the Enrollment Domain (ED) in illumination, pose, low-resolution, and blurriness. This paper proposed a method that deals with all problems related to face recognition with SSPP. 3D face reconstruction is used to increase the reference gallery set with different poses and generate a design domain dictionary to overcome the problem of limited reference. Besides, the design domain dictionary is used to feed different deep learning models. Face illumination transfer techniques are utilized to overcome the illumination problem. Labeled Faces in the Wild (LFW) dataset is used to train Super-Resolution Generative Adversarial Network (SRGAN) to overcome the low-resolution problem. Deblur Generative Adversarial Network (DeblurGAN) is trained on the LFW dataset to overcome the problem of blurriness. The proposed method is evaluated using the Chokepoint dataset and COX-S2V dataset. The final results confirm an overall enhancement in accuracy compared to techniques that use SSPP for face recognition (generic learning and face synthesizing approaches). Also, the proposed method outperforms of Traditional and Deep Learning (TDL) method accuracy, which uses SSPP for face recognition.

Deep learning, in particular Convolutional Neural Network (CNN), has achieved promising results in face recognition recently. However, it remains an open question: why CNNs work well and how to design a 'good' architecture. The existing works tend to focus on reporting CNN architectures that work well for face recognition rather than investigate the reason. In this work, we conduct an extensive evaluation of CNN-based face recognition systems (CNN-FRS) on a common ground to make our work easily reproducible. Specifically, we use public database LFW (Labeled Faces in the Wild) to train CNNs, unlike most existing CNNs trained on private databases. We propose three CNN architectures which are the first reported architectures trained using LFW data. This paper quantitatively compares the architectures of CNNs and evaluate the effect of different implementation choices. We identify several useful properties of CNN-FRS. For instance, the dimensionality of the learned features can be significantly reduced without adverse effect on face recognition accuracy. In addition, traditional metric learning method exploiting CNN-learned features is evaluated. Experiments show two crucial factors to good CNN-FRS performance are the fusion of multiple CNNs and metric learning. To make our work reproducible, source code and models will be made publicly available.