Framework For Face Recognition Research Articles

Enhancing and Securing a Real-Time Embedded Face Recognition System using Raspberry Pi

The modern world is full of data of all kinds; however, the vast amount of video and image data available provides the data set needed for facial recognition. Facial recognition is crucial in safety and surveillance systems that analyze visual data and millions of images. Using facial recognition software, a person's identity can be verified through a variety of media images. For facial recognition, there is a variety of algorithms available. The article presents an approach to a face recognition framework using Haar Cascade, a biometric technology in safety and surveillance systems. It investigates combining standard machine learning techniques for face detection and identification with Raspberry Pi face detection, a cost-effective and easy-to-use embedded system. The system detects faces from indirect and direct images, achieving high speed using the latest Raspberry Pi 4 and Python libraries. The work demonstrates a machine-learning-based design method and a complete embedded system. The face detection accuracy is 92%, and the average time is 0.35 compared to the local binary model (LBP). Many facial recognition algorithms on the web and in literature reviews are vulnerable to image attacks. These methods are very effective in identifying faces in webcams, video streams, images, and videos. This system's use of the Raspberry Pi 4 and advanced Python libraries results in fast and accurate real-time face detection. This paper extends the work first presented at the 12th Iranian/Second International Conference on Machine Vision and Image Processing (MVIP).

Face-Inception-Net for Recognition

Face recognition in general scenarios has been saturated in recent years, but there is still room to enhance model performance in extreme scenarios and fairness situations. Inspired by the successful application of Transformer and ConvNet in computer vision, we propose a FIN-Block, which gives a more flexible composition paradigm for building a novel pure convolution model and provides a foundation for constructing a new framework for general face recognition in both extreme scenarios and fairness situations. FIN-Block-A uses a combination of stacked large-size convolution kernels and parallel convolution branches to ensure a large spatial receptive field while improving the module’s deep feature embedding and extraction capabilities. FIN-Block-B takes advantage of stacked orthogonal convolution kernels and parallel branches to balance model size and performance. By applying FIN-Block with an adapted convolution kernel size in different stages, we built a reasonable and novel framework Face-Inception-Net, and the performance of the model is highly competitive with ConvNeXt and InceptionNeXt. The models were trained on CASIA-WebFace and MS-wo-RFW databases and evaluated on 14 mainstream benchmarks, including LFW, extreme scene, and fairness test sets. The proposed Face-Inception-Net achieved the highest average TAR@FAR0.001 of 95.9% in all used benchmarks, fully demonstrating effectiveness and generality in various scenarios.

Attention-Mechanism-Based Face Feature Extraction Model for WeChat Applet on Mobile Devices

Face recognition technology has been widely used with the WeChat applet on mobile devices; however, facial images are captured on mobile devices and then transmitted to a server for feature extraction and recognition in most existing systems. There are significant security risks related to personal information leakage with these transmissions. Therefore, we propose a face recognition framework for the WeChat applet in which face features are extracted in WeChat by the proposed Face Feature Extraction Model based on Attention Mechanism (FFEM-AM), and only the extracted features are transmitted to the server for recognition. In order to balance the prediction accuracy and model complexity, the structure of the proposed FFEM-AM is lightweight, and Efficient Channel Attention (ECA) was introduced to improve the prediction accuracy. The proposed FFEM-AM was evaluated using a self-built database and the WeChat applet on mobile devices. The experiments show that the prediction accuracy of the proposed FFEM-AM was 98.1%, the running time was less than 100 ms, and the memory cost was only 6.5 MB. Therefore, this demonstrates that the proposed FFEM-AM has high prediction accuracy and can also be deployed with the WeChat applet.

Deep learning framework for real-time face recognition using multimodal facial expression with feature optimization and hybrid classification

Smile detection plays key role in recognizing human facial expressions, especially in inter-personal relations. In modern biometrics and security applications, face recognition is widely used. In real-life applications, automated face recognition has always been challenging because people express themselves differently. Real-world scenarios present many challenges, such as low-quality images, temporal variations, and disguises that alter facial characteristics. Face recognition has been accomplished using deep learning frameworks, but there are still several challenges to overcome, such as expression variations and lack of training data. In these methods, margins are used to enforce intra-class compactness and inter-class discrepancy to prevent overfitting. Multimodal facial expression detection is used for real-time face recognition to solve the aforementioned problems. First, we utilizes the Prewitt edge detector for eyes, nose, mouth, and brow detection, and design a deep transfer learning model to extract deep features from face images. Next, feature optimization is done by harpy eagle search optimization (HESO) to selects optimal best among multiple features which reduce data dimensionality problem. Then, we employ a semi-region based convolutional neural network (SR-CNN) to analyzes the facial expressions from multimodalities such as eyes (blinking), nose (shapes), mouth (smiling), and brow (shapes). The analyzed facial expressions and corresponding input images are fed into the benchmark SVM classifier for face recognition. The facial expression based face recognition framework is most suited for real-time scenario like video surveillance. Finally, we validate the performance our framework using benchmark multi-modal Cohn-Kanade and real-time 5671 person images datasets. The simulation results show that the proposed framework can effectively suppress data dimensionality issues and achieve high recognition rate compare to state-of-art frameworks.

Face Plastic Surgery Recognition Model Based on Neural Network and Meta-Learning Model 

Facial recognition is a procedure of verifying a person's identity by using the face, which is considered one of the biometric security methods. However, facial recognition methods face many challenges, such as face aging, wearing a face mask, having a beard, and undergoing plastic surgery, which decreases the accuracy of these methods. This study evaluates the impact of plastic surgery on face recognition models. The motivation for conducting the research in that aspect is because plastic surgery treatments do not only change the shape and texture of any face but also have increased rapidly in this era. This paper proposes a model based on an artificial neural network with model-agnostic meta-learning (ANN-MAML) for plastic surgery face recognition. This study aims to build a framework for face recognition before and after undergoing plastic surgery based on an artificial neural network. Also, the study seeks to clarify the collaboration between facial plastic surgery and facial recognition software to determine the issues. The researchers evaluated the proposed ANN-MAML's performance using the HDA dataset.  The experimental results show that the proposed ANN-MAML learning model attained an accuracy of 90% in facial recognition using Rhinoplasty (Nose surgery) images, 91% on Blepharoplasty surgery (Eyelid surgery) images, 94% on Brow lift (Forehead surgery) images, as well as 92% on Rhytidectomy (Facelift) images. Finally, the results of the proposed model were compared with the baseline methods by the researchers, which showed the superiority of the ANN-MAML over the baselines. 

ResNet and PCA-Based Deep Learning Scheme for Efficient Face Recognition

Face recognition is an emerging field of research in recent days. With the rise of deep learning, face recognition has become efficient and precise, creating new milestones. The performance, accuracy, and computational time of the existing schemes can be enhanced by devising a new scheme. In this context, a multiclass classification framework for face recognition using residual network (ResNet) and principal component analysis (PCA) schemes of deep learning with Dlib library is proposed in this paper. The proposed framework produces face recognition accuracy of 99.6% and a reduction of computational time with 68.03% using principal component analysis.

A face recognition taxonomy and review framework towards dimensionality, modality and feature quality

This paper presents a comprehensive review of Automatic Facial Recognition Systems using integrative and systematic mapping approach. The review is grounded on criteria-attribute scheme formulated in proposed Face Recognition framework. The proposed framework provides a unified platform to identify, categorize and understand wholesome Face Recognition taxonomy based on different criteria (Modality, Dimensionality and Feature Quality) and their corresponding attributes (Unimodal-Multimodal, 2D-3D and Physiological-Behavioral). The framework facilitates a user to understand and select attributes across different criteria. The user selection of criteria-attribute is assisted through several selection parameters (Dataset Availability, Application, User Preference, System Complexity and Time Complexity). Depending on the user selection, a criteria-attribute scheme based model is formulated for Face Recognition. This paper also provides critical mathematical insights to understand each attribute extensively. Existing works are analyzed and compared comprehensively and quantitatively based on popular datasets and proposed criteria-attribute framework.

Next-generation attendance tracking: Automated face detection for lecture attendance

Face detection technology has garnered much interest due to its potential uses in industries like face identification and video surveillance systems. In addition to being a component of the automatic face recognition framework, real-time face detection is now becoming its study area. There are numerous methods to handle the problem of face detection. Traditional methods of manual attendance tracking in lecture halls are time-consuming, prone to errors, and lack efficiency. Next-generation attendance tracking systems are incorporating automated face detection technology to address these challenges. This paper explores the implementation of automated face detection for lecture attendance, leveraging computer vision and facial recognition algorithms. Initially, students’ data were gathered and preprocessed using a Median filter (MF). Then the preprocessed data features were extracted using a histogram of gradients (HOG). And finally, extracted features were classified using a Backpropagation Bayesian neural network (BBNN). To prove the efficiency of the suggested method, that is compared and contrasted with specific conventional methods. Experimental outcomes show that the recommended method performs superior when contrasted to traditional methods.

Federated Multidomain Learning With Graph Ensemble Autoencoder GMM for Emotion Recognition

Facial expression cognition technology continues to face challenges from certain perspectives despite the fact that there have been significant recent learning advances in computer vision in the areas involving posture, orientation, and viewing mode of photos or videos that affects the device performance. In particular, the current distributed machine learning schemes do not consider the privacy issue in face monitoring data. Hence, this paper proposes a new federated learning framework for unsupervised multidomain face recognition of postexercise. It is a graph AE design base to ensure multiple edge devices can cooperate with each other to ensure the optimization of the common objective function of the model to enhance the efficiency and speed of the global model. In addition, a multidomain learning loss function is proposed to share the common feature representation with other related tasks to improve domain adaptability. Adversarial learning is used to improve the recognition effect of the federated framework in each domain. The proposed scheme is validated on different multidomains expression datasets and the experimental results indicate a 19% higher F1 score than the benchmark scheme in multidomain face recognition tasks.

Framework for facial recognition and reconstruction for enhanced security and surveillance monitoring using 3D computer vision

Framework for facial recognition and reconstruction for enhanced security and surveillance monitoring using 3D computer vision

AI Based Content Controlling System using Age Prediction Algorithm and Selenium Tool

Abstract: The internet provides a platform to distribute a vast amount of content to millions of users worldwide. However, the true issue is when users under the age of 18 are denied access to age-restricted content, which over time has a negative impact on the user's physical and mental health. Children are spending more amount of time in surfing the browser and they easily come across all types of content in internet. On the other hand, creating a plan for computing imaginative complexity would ensure children's safety in social networks, which is unmatched among the most important criteria in today's society. This technique will only be used to regulate the website's content. In this proposed work, face detection has been carried out for detecting the age using a Haas cascade classifier and content restriction based on age done by selenium. In general, two sophisticated techniques, such as age estimation and facial recognition, are needed to limit internet access to specified age groups. Then, for a content management system, this proposed work has suggested a new unified framework for face recognition and age detection. This proposed work has developed a system that accurately controls the browser content by combining age recognition and content restriction. In order to regulate the website, selenium is applied after detecting the person’s face and estimating their age.

Sample Reduction-Based Pairwise Linear Regression Classification for IoT Monitoring Systems

At present, the development of the Internet of Things (IoT) has become a significant symbol of the information age. As an important research branch of it, IoT-based video monitoring systems have achieved rapid developments in recent years. However, the mode of front-end data collection, back-end data storage and analysis adopted by traditional monitoring systems cannot meet the requirements of real-time security. The currently widely used edge computing-based monitoring system can effectively solve the above problems, but it has high requirements for the intelligent algorithms that will be deployed at the edge end (front-end). To meet the requirements, that is, to obtain a lightweight, fast and accurate video face-recognition method, this paper proposes a novel, set-based, video face-recognition framework, called sample reduction-based pairwise linear regression classification (SRbPLRC), which contains divide SRbPLRC (DSRbPLRC), anchor point SRbPLRC (APSRbPLRC), and attention anchor point SRbPLRC (AAPSRbPLRC) methods. Extensive experiments on some popular video face-recognition databases demonstrate that the performance of proposed algorithms is better than that of several state-of-the-art classifiers. Therefore, our proposed methods can effectively meet the real-time and security requirements of IoT monitoring systems.

Framework for Posture and Face Recognition Using Kinect an Ambient-intelligence Method

This electronic document describes one of the first full open-source frameworks for posture and face detection and classification using Kinect sensors, based entirely on an open-sourced software platform. It has been designed with the possibility of automatic monitoring of either an elderly person who is situated in a closed space (a room with furniture, appliances, and other rigid objects), or a patient who suffers from a physiological disorder. It is also based on an original hardware platform (the AmI-Platform), which includes more Kinect sensors, data acquisition stations, and crunches (data servers). The articles tackle two of the most important issues for ambient intelligence: accurate determination of the person and his or her facial landmarks (to understand emotions) and fast and reliable posture determination (to take appropriate measures in case of an accident).

PriFace: a privacy-preserving face recognition framework under untrusted server

PriFace: a privacy-preserving face recognition framework under untrusted server

Learning Streamed Attention Network from Descriptor Images for Cross-Resolution 3D Face Recognition

In this article, we propose a hybrid framework for cross-resolution 3D face recognition which utilizes a Streamed Attention Network (SAN) that combines handcrafted features with Convolutional Neural Networks (CNNs). It consists of two main stages: first, we process the depth images to extract low-level surface descriptors and derive the corresponding Descriptor Images (DIs), represented as four-channel images. To build the DIs, we propose a variation of the 3D Local Binary Pattern (3DLBP) operator that encodes depth differences using a sigmoid function. Then, we design a CNN that learns from these DIs. The peculiarity of our solution consists in processing each channel of the input image separately, and fusing the contribution of each channel by means of both self- and cross-attention mechanisms. This strategy showed two main advantages over the direct application of Deep-CNN to depth images of the face; on the one hand, the DIs can reduce the diversity between high- and low-resolution data by encoding surface properties that are robust to resolution differences. On the other, it allows a better exploitation of the richer information provided by low-level features, resulting in improved recognition. We evaluated the proposed architecture in a challenging cross-dataset, cross-resolution scenario. To this aim, we first train the network on scanner-resolution 3D data. Next, we utilize the pre-trained network as feature extractor on low-resolution data, where the output of the last fully connected layer is used as face descriptor. Other than standard benchmarks, we also perform experiments on a newly collected dataset of paired high- and low-resolution 3D faces. We use the high-resolution data as gallery, while low-resolution faces are used as probe, allowing us to assess the real gap existing between these two types of data. Extensive experiments on low-resolution 3D face benchmarks show promising results with respect to state-of-the-art methods.

More learning with less labeling for face recognition

In this paper, we propose an improved face recognition framework where the training is started with a small set of human annotated face images and then new images are incorporated into the training set with minimum human annotation effort. In order to minimize the human annotation effort for new images, the proposed framework combines three different strategies, namely self-paced learning (SPL), active learning (AL), and minimum sparse reconstruction (MSR). As in the recently proposed ASPL framework [1], SPL is used for automatic annotation of easy images, for which the classifiers are highly confident and AL is used to request the help of an expert for annotating difficult or low-confidence images. In this work, we propose to use MSR to subsample the low-confidence images based on diversity using minimum sparse reconstruction in order to further reduce the number of images that require human annotation. Thus, the proposed framework provides an improvement over the recently proposed ASPL framework [1] by employing MSR for eliminating “similar” images from the set selected by AL for human annotation. Experimental results on two large-scale datasets, namely CASIA-WebFace-Sub and CACD show that the proposed method called ASPL-MSR can achieve similar face recognition performance by using significantly less expert-annotated data as compared to the state-of-the-art. In particular, ASPL-MSR requires manual annotation of only 36.10% and 54.10% of the data in CACD and CASIA-WebFace-Sub datasets, respectively, to achieve the same face recognition performance as the case when the whole training data is used with ground truth labels. The experimental results indicate that the number of manually annotated samples have been reduced by nearly 4% and 2% on the two datasets as compared to ASPL [1].

Pig Face Recognition Based on Metric Learning by Combining a Residual Network and Attention Mechanism

As machine vision technology has advanced, pig face recognition has gained wide attention as an individual pig identification method. This study establishes an improved ResNAM network as a backbone network for pig face image feature extraction by combining an NAM (normalization-based attention module) attention mechanism and a ResNet model to probe non-contact open-set pig face recognition. Then, an open-set pig face recognition framework is designed by integrating three loss functions and two metrics to finish the task with no crossover of individuals in the training and test sets. The SphereFace loss function with the cosine distance as a metric and ResNAM are combined in the framework to obtain the optimal open-set pig face recognition model. To train our model, 37 pigs with a total of 12,993 images were randomly selected from the collected pig face images, and 9 pigs with a total of 3431 images were set as a test set. 900 pairs of positive sample pairs and 900 pairs of negative pairs were obtained from the images in the test set. A series of experimental results show that our accuracy reached 95.28%, which was 2.61% higher than that of a human face recognition model. NAM was more effective in improving the performance of the pig face recognition model than the mainstream BAM (bottleneck attention module) and CBAM (convolutional block attention module). The research results can provide technological support for non-contact open-set individual recognition for intelligent farming processes.

Missing Person Identification

At present the Computer automated Face recognition systems are used for personal identification, but the Age variations of an individual poses a serious problem for it. Designing an appropriate feature representation and an effective matching framework for age invariant face recognition remains an open problem. To classify person age using faces author using combination of two CNN where one CNN will extract face features which can help in identify changes in face over time and second CNN helps in predicting/ classifying age. Face aging causes intra-subject variations (such as geometric changes during childhood & adolescence, wrinkles and saggy skin in old age) which negatively affects the accuracy of face recognition systems. Therefore, this paper proposes a unified, multi-task framework to jointly handle these two tasks, termed MTLFace, which can learn age-invariant identity-related representation while achieving pleasing face synthesis. Specifically, we first decompose the mixed face features into two uncorrelated components—identity- and age-related features—through an attention mechanism, and then decorrelate these two components using multi-task training and continuous domain adaption. d. This system will decrease the crimes and ensure the security in our society.

A Novel Framework of Face Recognition using Heuristic Development of Ensemble Classifier Model

A Novel Framework of Face Recognition using Heuristic Development of Ensemble Classifier Model

A Symmetrical Siamese Network Framework With Contrastive Learning for Pose-Robust Face Recognition.

Face recognition has achieved remarkable success owing to the development of deep learning. However, most of existing face recognition models perform poorly against pose variations. We argue that, it is primarily caused by pose-based long-tailed data - imbalanced distribution of training samples between profile faces and near-frontal faces. Additionally, self-occlusion and nonlinear warping of facial textures caused by large pose variations also increase the difficulty in learning discriminative features of profile faces. In this study, we propose a novel framework called Symmetrical Siamese Network (SSN), which can simultaneously overcome the limitation of pose-based long-tailed data and pose-invariant features learning. Specifically, two sub-modules are proposed in the SSN, i.e., Feature-Consistence Learning sub-Net (FCLN) and Identity-Consistence Learning sub-Net (ICLN). For FCLN, the inputs are all face images on training dataset. Inspired by the contrastive learning, we simulate pose variations of faces and constrain the model to focus on the consistent areas between the original face image and its corresponding virtual pose face images. For ICLN, only profile images are used as inputs, and we propose to adopt Identity Consistence Loss to minimize the intra-class feature variation across different poses. The collaborative learning of two sub-modules guarantees that the parameters of network are updated in a relatively equal probability between near-frontal face images and profile images, so that the pose-based long-tailed problem can be effectively addressed. The proposed SSN shows comparable results over the state-of-the-art methods on several public datasets. In this study, LightCNN is selected as the backbone of SSN, and existing popular networks also can be used into our framework for pose-robust face recognition.