Traditional Face Recognition Methods Research Articles

Device-edge collaborative occluded face recognition method based on cross-domain feature fusion

The lack of facial features caused by wearing masks degrades the performance of facial recognition systems. Traditional occluded face recognition methods cannot integrate the computational resources of the edge layer and the device layer. Besides, previous research fails to consider the facial characteristics including occluded and unoccluded parts. To solve the above problems, we put forward a device-edge collaborative occluded face recognition method based on cross-domain feature fusion. Specifically, the device-edge collaborative face recognition architecture gets the utmost out of maximizes device and edge resources for real-time occluded face recognition. Then, a cross-domain facial feature fusion method is presented which combines both the explicit domain and the implicit domain facial. Furthermore, a delay-optimized edge recognition task scheduling method is developed that comprehensively considers the task load, computational power, bandwidth, and delay tolerance constraints of the edge. This method can dynamically schedule face recognition tasks and minimize recognition delay while ensuring recognition accuracy. The experimental results show that the proposed method achieves an average gain of about 21% in recognition latency, while the accuracy of the face recognition task is basically the same compared to the baseline method.

Studies advanced in face recognition technology based on deep learning

Face recognition technology has always been a hot research topic in the computer vision community, and has developed rapidly in recent years. Face recognition aims to build a model and predict the face identity information in a given image, which has been widely used in various aspects of social life, such as identity authentication, security encryption, human-computer interaction, etc. In order to improve the accuracy and speed of face recognition, and how to maintain good face recognition under the premise of occlusion, many advanced technologies have been proposed. This paper summarizes the face recognition technologies proposed in recent years, and introduces the latest research progress in the field of face recognition from two aspects: traditional face recognition based on manual features and face recognition based on deep learning. Specifically, we first briefly introduce traditional face recognition methods. Second, we introduce the mechanism of traditional Convolutional Neural Networks(Hereinafter referred to as CNN) in face recognition. Finally, we focus on the application of Transformer in the field of face recognition. According to the datasets used by the methods introduced above, the performance of these methods is summarized, the advantages and disadvantages of CNN and Transformer are pointed out, and the future development direction is proposed.

VGG16 based on dilated convolution for face recognition

Face recognition has wide applications in fields such as security systems, biometrics, and human-computer interaction. However, traditional face recognition methods face challenges in capturing details and reducing model complexity. To address these issues, this paper proposes a new method based on VGG16, which improves recognition accuracy and reduces parameter quantity by introducing dilated convolution and parameter pruning. First, the hole convolution is introduced to expand the Receptive field and capture more details to enhance the ability of the model in distinguishing facial features. Next, parameter pruning is applied to reduce redundant parameters, optimize model structure, and improve computational efficiency. This article conducted experimental evaluation on the classic face recognition dataset CK+ dataset. The results show that the proposed method is significantly superior to the traditional VGG16 model in terms of recognition accuracy. At the same time, the use of pruning technology significantly reduces the number of parameters in the model and improves computational efficiency. The experimental outcomes conclusively validate the effectiveness and feasibility of the proposed method.

Cyclic style generative adversarial network for near infrared and visible light face recognition

Near Infrared and Visible Light (NIR-VIS) face recognition attracts attention from researchers because of its potential for safety, illumination invariance, and stability. Nevertheless, the difference between NIR and VIS domains, the domain gap, remains a huge problem for matching NIR and VIS images. Specifically, for the same identity, the appearance in the NIR domain is different from the VIS domain. Thus, traditional face recognition methods cannot be effective. To address this problem, this paper proposes a novel model, called Cyclic-Style GAN (CS-GAN). First, the pre-trained Style-GAN 3 network is embedded into the Cycle-GAN structure for NIR-VIS cross-domain learning. Second, there is a cyclic subspace learning method consisting of latent loss and style loss, through which both style (domain feature) and facial characteristic features are learned to improve the quality of synthesized images. The model synthesizes realistic VIS images from NIR images and does the face recognition task in the VIS domain. The proposed method achieves 99.6% Rank-1 accuracy on the CASIA NIR-VIS 2.0 database which is a state-of-the-art result. The visualization results show that the proposed model synthesizes VIS images with a clear texture of faces and in close-to-reality color.

CNN combined with data augmentation for face recognition on small dataset

Faces have universal structures yet contain distinct features among individuals. Recognizing individuals based on their faces has always been a popular topic in pattern recognition, and computer vision and many traditional approaches have yielded satisfying results. In recent years, rapid growth in deep learning has encouraged researchers to use deep learning methods to solve authentication problems. Convolutional neural networks are one of the most popular deep neural networks with multiple layers and the ability to reduce parameters by using kernels to capture features from input. It has outstanding performance in pattern recognition due to its ability to extract features and take images as inputs. In machine learning, data augmentation is a technique to seemingly enlarge a dataset to avoid underfitting or overfitting problems caused by insufficient data. This paper uses convolutional neural networks to solve face recognition problems on a small dataset. It compares performance with traditional face recognition methods such as Principal Component Analysis and examines the impact on performance using data augmentation. Overall, data augmentation boosts the accuracy of the network but also results in an unsteady learning curve. The convolutional neural network performs well on pattern recognition and obtains an accuracy of 94% in an augmented dataset with only two convolutional layers.

Studies Advanced in Mask Face Recognition based on Deep Learning

In the era of epidemic, wearing a mask when going out has become a more common phenomenon. And how to complete face recognition and guarantee certain accuracy without letting users take off their masks has been a challenge in recently years’ face recognition field. In this paper, the traditional masked face recognition method and the new MTCNN deep learning masked face recognition method are compared and analyzed, and other similar masked experiments are introduced as data references. It is synthetically found that the accuracy of MTCNN masked face recognition using CASIA-FaceV5 as dataset is more than 90%, which is far better than other traditional recognition methods, but its requirement for dataset image quality is high, and the training time is long and costly. We hope that the process analysis and result comparison of different methods in this paper can shed light on the future development of masked face recognition technology.

A Survey of Face Recognition Methods based on Deep Learning

Since the 21st century, people have put forward higher requirements for information security technology in many fields such as society and economy. The requirement has promoted the development of biometric identification technology. Face recognition technology has become a research hotspot in biometric identification technology with its unique advantages. In recent years, with the development of deep learning, face recognition technology has made breakthroughs. Face recognition technology based on deep learning has been widely used in various fields such as finance, education, security, transportation, and new retail. In the process of face recognition technology becoming popular, some comprehensive literatures are urgently needed to summarize the methods of face recognition technology. Based on this, this paper first introduces the principle and existing problems of traditional face recognition methods, and then introduces in detail two typical face recognition methods based on deep learning—face recognition methods based on convolutional neural networks and face recognition methods based on deep belief networks. Finally, we provide an overview of common face datasets.

Sketch face recognition based on domain adaptation scaled entropy meta-network

In recent years, sketch face recognition has a wide application in law enforcement agencies and criminals. Deep learning plays a crucial role in the recent developments of face recognition, however, it is challenging to employ deep learning methods for sketch face recognition due to insufficient face photo–sketch data. Moreover, compared to photos, sketches lack detailed texture, and there exists a domain gap between photos and sketches, hence, traditional homogeneous face recognition methods perform poorly in sketch face recognition. In this paper, a novel deep learning method termed Domain Adaptation Scaled Entropy Meta-Network (DASEMN) is proposed to tackle sketch face recognition tasks. Specifically, a meta-learning training strategy is designed to tackle the few-shot problem and improve the generalization ability of the network. Then, a generalized entropy loss termed scaled mean entropy loss is proposed to guide the network to extract discriminate features. Finally, a domain adaptation module is introduced in the training set to reduce the domain gap between the sketch domain and the photo domain. Experiments on UoM-SGFS and CUFSF sketch face databases show that the proposed method is superior to other sketch face recognition methods.

Face Recognition Model Design Based on Convolutional Neural Networks

With the rapid development of computer science and Internet technology, face recognition technology is widely used in such as public security, judicial and criminal investigation, public security, information security and access control system, such as public security system need to find out the criminals in the system library, or from the entrance control system quickly identify and match the identity of relevant personnel information. As a stable, intuitive and highly recognizable biometric feature, human face is being paid more and more attention by researchers. Compared with other bioinformation recognition methods, face recognition is characterized by direct, friendliness and convenience. Users are not easy to resist, and compared with other recognition technologies, they are easy to be accepted by users, so it has received attention and research. This paper designs a face recognition system based on a convolutional neural network. Compared with the traditional face recognition method, the convolutional neural network model does not need manual complex and time-consuming feature extraction algorithm design, only need to design an effective neural network model, and then end-to-end training on a large number of training samples for simple and efficient training, can get a good identification effect. The design uses the target detection algorithm to conduct accurate, real-time, efficient face recognition, and can accurately identify the people in the camera.

Research on neural network algorithm in artificial intelligence recognition

With the rapid development of computer application technology, computer artificial intelligence recognition technology has also made rapid progress. Artificial intelligence was first represented by LISP language, machine theorem proof, etc., and then the intelligent medical treatment and neural network appeared, and now it realizes the popularization of AI. The purpose of this article is to explore the application of neural network algorithms in artificial intelligence recognition. Based on the classic convolutional neural network with LeNet structure, this paper constructs a reasonable convolutional neural network model by deepening and expanding, and uses it in face recognition to conduct face recognition research. LeNet is the origin of CNN, and LeNet is mainly used to identify and classify handwritten characters. This paper uses three networks on the YaleB face database for face recognition, and compares the pooling method of the maximum pooling and average pooling, the misclassification rate of the ReLU incentive function and the tanh incentive function on the three networks.YaleB is a very complete face database, divided into five subsets, ten people, and 64 images per person, including changes in illumination, expressions, and gestures. The tanh function has what is an odd function and has a soft saturation property, while the relu function has a hard saturation property. Analyze whether it can solve the problems caused by illumination factors and occlusion factors in traditional face recognition. Finally, the recognition effect is compared with the traditional face recognition method, and it is concluded that the effect of the traditional face recognition method is not as high as the misclassification rate of 0.0356 based on the convolutional neural network. The recognition method works well. Maximum pooling is also known as maximum reduced pixel sampling, and is more based on the input element map than average pooling.Through the optimization of the algorithm, the face recognition rate is improved, which helps the application and development of artificial intelligence.

Application of Deep Learning and Speech Recognition Technology for Pedestrian Face Recognition in health sectors

Aiming at the problems of traditional face recognition methods, this paper proposes the application of deep learning and speech recognition technology in pedestrian face recognition. Firstly, the pedestrian face image information is collected, and the face image is decomposed by wavelet scale. The improved detail enhanced face image is obtained, and Harris adaptive threshold corner detection is performed on the enhanced face image. The feature points of pedestrian face image is extracted and matched, and the local radial transformation of points and lines and the Epipolar constraint between multiple planes are adopted. Combined with the constraints of the angle and gray approximation measure of the line features of the face image, the line matching of the face close range image is completed. The 3D line features of the pedestrian face image are extracted and fitted by using the principle of face to face intersection. Combined with the pedestrian face image recognition algorithm, the pedestrian face recognition is realized. The experimental results show that the pedestrian face recognition method based on deep learning and speech recognition technology has better performance.

LocalFace: Learning significant local features for deep face recognition

As a widely mentioned topic in face recognition, the margin-based loss function enhances the discriminability of face recognition models by applying margin between class decision boundaries. However, there is still room to improve the representation of face features. Local face feature extraction has been employed in traditional face recognition methods, but with the increase of network depth in deep learning, the traditional approach requires a large number of computational resources. In this paper, we propose a novel face recognition architecture called LocalFace to extract local face features. First, by analyzing the distribution of significant features in face images, we propose an efficient face fixed-point local feature extraction approach and improve this method to propose a more effective face dynamic local feature extraction scheme. Subsequently, we propose a block-based random occlusion method for the limitations of the random face occlusion method to better simulate the occlusion situation in real scenes. In the end, we present a detailed discussion on the channel attention method that is more appropriate for face recognition and classification tasks. Our method enhances the representation of face features by ensembling local features into global features without extra parameters, which is efficient and easy to implement. Extensive experiments on various benchmarks demonstrate the superiority of our LocalFace, and part of the experimental results achieve SOTA results.

Deblurring Method of Face Recognition AI Technology Based on Deep Learning

As a common method of deep learning, a convolutional neural network (CNN) shows excellent performance in face recognition. The features extracted by traditional face recognition methods are greatly influenced by subjective factors and are time-consuming and laborious. In addition, these images are susceptible to illumination, expression, occlusion, posture, and other factors, which bring a lot of interference to the computer face recognition and increase recognition difficulty. Deep learning is the most important technical means in the field of computer vision. The participation of this technology reduces manual participation and can identify the identity of visitors from multiple aspects. This study, based on the introduction at all levels and on the fundamental principle of the colloidal neural network, combines the basic model and the common exploitation methods of aspects to make a model of a combination of multiple aspects. Then, an improved CNN-based multifeature fusion face recognition model is proposed, and the effectiveness of the model in face feature extraction is verified by experiments. With the experimental results, the identification rate for the ORL and Yale data sets is 98.2% and 98.8%, respectively. Accordingly, an online face detection system and recognition system based on the combination of element models are designed. The system can obtain dynamic facial recognition and meet the recognition rate of the design requirements. The system is training four detection models and online recognition, and the test results show that the noise component model has the highest recognition rate, and the recognition rate has improved by 13% compared with the baseline capacity, further verifying that a model of a combination of features can achieve more effectively.

Recognition method of unspecified face expressions based on machine learning

Traditional face recognition methods usually complete facial expression recognition for designated faces, and the pixel set at the edge of face image is chaotic, which leads to poor accuracy of unspecified facial expression recognition. In order to improve the accuracy of unknown facial expression recognition, a method of unknown facial expression recognition based on machine learning is proposed. The feature detection model of unspecified facial expressions is constructed, and the features are divided into regional blocks. Fusion block feature information establishes a spatial feature projection model, weights the feature information entropy, extracts statistical features and edge information entropy features, reorganises features and matching edge pixel sets and completes the recognition of various facial expression features. Experimental results show that the accuracy of this method is significant, reaching 1, which effectively improves the recognition efficiency and anti-interference performance.

The model of fast face recognition against age interference in deep learning

In order to overcome the low recognition efficiency of traditional anti-age face recognition methods, the paper proposes a new anti-age-disturbing face recognition modelling method based on deep learning. Firstly, build a standard face recognition information database and use this as the matching standard for face recognition. Then, construct a deep learning convolutional neural network, install the propagation process and training strategy of deep learning. Lastly, build an age discrimination model, a loss function and an objective function and solve it. On this basis, the facial features are extracted, after matching with the data in the standard database and similarity calculation, the final rapid facial recognition result is obtained. Experimental results show that the highest recognition accuracy of the designed face recognition model is 99.2%, and the recognition speed of the designed model is faster.

A Real-Time CNN-Based Lightweight Mobile Masked Face Recognition System

Due to the global spread of the Covid-19 virus and its variants, new needs and problems have emerged during the pandemic that deeply affects our lives. Wearing masks as the most effective measure to prevent the spread and transmission of the virus has brought various security vulnerabilities. Today we are going through times when wearing a mask is part of our lives, thus it is very important to identify individuals who violate this rule. Besides, this pandemic makes the traditional biometric authentication systems less effective in many cases such as facial security checks, gated community access control, and facial attendance. So far, in the area of masked face recognition, a small number of contributions have been accomplished. It is definitely imperative to enhance the recognition performance of the traditional face recognition methods on masked faces. Existing masked face recognition approaches are mostly performed based on deep learning models that require plenty of samples. Nevertheless, currently, there are not enough image datasets that contain a masked face. As such, the main objective of this study is to identify individuals who do not use masks or use them incorrectly and to verify their identity by building a masked face dataset. On this basis, a novel real-time masked detection service and face recognition mobile application were developed based on an ensemble of fine-tuned lightweight deep Convolutional Neural Networks (CNN). The proposed model achieves 90.40% validation accuracy using 12 individuals’ 1849 face samples. Experiments on the five datasets built in this research demonstrate that the proposed system notably enhances the performance of masked face recognition compared to the other state-of-the-art approaches.

Facial Expression Recognition Method Combined with Attention Mechanism

Aiming at the slow speed and low accuracy of traditional facial expression recognition, a new method combining the attention mechanism is proposed. Firstly, group convolution is used to reduce network parameters. The channels of traditional convolution are grouped to cut off redundant connections so that the number of parameters decreases significantly. Secondly, the ERFNet network model was improved by combining the asymmetric residual module and the weak bottleneck module to improve the running speed and reduce the loss of accuracy. Finally, the attention mechanism was added into the feature extraction network to improve the recognition precision. The experiment shows that compared with traditional face recognition methods, the proposed method can improve the recognition precision and recall significantly; in CK+, Jaffe, and Fer2013 datasets, the recognition precision can reach 88.81%, 82.16%, and 79.33%, respectively.

Research on Face Recognition Algorithm Based on Block CR

Partial absence of face information challenges the robustness of face recognition algorithms. In order to reduce the effect of partial information loss on face recognition, a Face Recognition Method based on partitioning Collaborative Representation (FRAPCR) is proposed in this paper. Firstly, the face image is divided into several sub-blocks. Secondly, the Collaborative Representation (CR) is used to calculate the minimum sparse representation coefficient of each sub-block and the residual between the sub-block and the corresponding samples of each class, taking the class corresponding to the minimum residual as the class to which the sub-block belongs. Thirdly, a voting mechanism is introduced to count the categories of all sub-blocks of each face image, and the category with the largest number of votes is the category to which the whole face image belongs. Through the experiments on face databases ORL, Extend Yale B, and AR by the proposed method (FRAPCR), the best partitioning way of the face image is obtained. When there is partial information missing (pixel information missing, corrosion block and occlusion) in the face image, the images in each face database is divided in its corresponding optimal partitioning way. And comparative experiments between the FRAPCR and traditional CR face recognition methods are performed. The results show that FRAPCR has high recognition rate and stable recognition effect when there is partial information missing in face images.

Research on Electronic Devices Suitable for Epidemic Prevention and Control

Facing the normalization of epidemic situation in COVID-19 at present, how to effectively prevent and control the epidemic situation is very important. The traditional face recognition method is short in recognition distance and can’t recognize whether to wear a mask, so it is not suitable for today’s environment of epidemic prevention. In this paper, MLX90614 high precision infrared probe is used. According to the principle that the infrared energy is focused on the photodetector and converted into corresponding electrical signals, using gradient calibration and linear data acquisition method to program, the function of high-precision long-distance contactless body temperature measurement is realized. The face recognition function uses the camera to scan a number of key attributes in the face, uses advanced mathematics to carry out three-dimensional modeling, establishes the curvature, gradient, curl, angle and distance data corresponding to the feature points, and iteratively compares them with the feature values to realize the recognition of the face and whether to wear a mask or not. Finally, K210 intelligent chip is used as the main controller to connect with each part to realize its function, and the actual verification test is carried out, and good results are achieved.

Face recognition framework based on effective computing and adversarial neural network and its implementation in machine vision for social robots

In recent years, with the continuous breakthrough of computer vision technology, the accuracy of object detection and target recognition has been improved by leaps and bounds. Face recognition is one of the important research directions in the field of computer vision, which is widely used in mobile payment, safe city, criminal investigation and other fields. Traditional face recognition methods need to extract face image features manually. The extracted features are greatly affected by subjective factors, and time-consuming and laborious. Deep learning is the most important technology in the field of computer vision at present. Compared with traditional face recognition methods, it can extract more essential features of face image without manual participation. In this paper, we build a face recognition system based on neural computing model and the principle of neural network. The experimental results show that the proposed method has high detection rate and short processing time.