Lightweight FaceNet Based on MobileNet

An empirical study of the impact of masks on face recognition

The face recognition (FR) process identifies or confirms a person with the help of images and videos. The traditional aging FR (AFR) method encounters numerous issues as a result of age-related biological transformation. An adaptive fully recurrent deep neural learning (AFRDNL) technique is presented in this research to improve FR accuracy (FRA) with minimal time complexity (TC). Feature extraction and classification are two processes included in the proposed technique. The AFRDNL technique gathers facial images from the database and trains the face images with three layers. To learn the facial features, three hidden layers are employed in the AFRDNL technique. Finally, the AFRDNL technique utilizes the Gaussian activation function for matching extracted testing features with earlier saved training features. This process is repeated until a minimal training error with a higher FRA is achieved. Experimental evaluation is carried out with three image datasets like Face and Gesture Recognition Research Network, MORPH and Cross-Age Labeled faces in the Wild (CALFW) datasets using different metrics, such as FRA, false-positive rate (FPR), TC and F1score. The experimental results confirm that the AFRDNL technique effectively increases the FRA and F1score and minimizes the FPR and TC more effectively than the state-of-the-art methods.

Traditional attendance systems, such as manual sign-ins or RFID-based methods, are time- consuming, prone to errors, and susceptible to manipulation. This paper presents a Smart Attendance System that leverages facial recognition technology to automate attendance tracking. The system integrates advanced face detection and recognition algorithms, along with anti-spoofing mechanisms, to ensure secure and efficient attendance marking. The proposed system uses the OpenCV library for face detection, the FaceNet algorithm for facial recognition, and Fourier Spectrum Analysis for anti-spoofing. Built with Python and Tkinter, the system provides a user-friendly interface for real-time attendance logging and management. The results demonstrate high accuracy in face recognition, efficient processing, and robust security against spoofing attempts, making it a reliable solution for modern attendance management in educational and corporate environments.

Achieving low recognition time and high accuracy in real-time face recognition is challenging. This study implements Viola-Jones, Local Binary Pattern Histogram, and Euclidean Distance for real-time face recognition and calculates the face detection time. The face image is detected using the Viola-Jones method; its features are extracted using the Local Binary Pattern Histogram, and the face is recognized using Euclidean Distance. This study processes sample images from 1013 students as training data, with 20 images represent each student. The experiments show that 268 of 342 testing data are recognized correctly, resulting in an accuracy of 78.4%, with average real-time recognition time of 0.93 seconds.

Big data refers to huge information whose quantity is beyond the capacity of the system to manage, process and capture. On the basis of the biological and behavioral characteristics of the human, information gathered from which person can be recognized and referred as biometrics. Example includes finger print, face, voice and behavioral analysis etc. Among these, face recognition will not make use of any physical contact with the biometrics system; it is more secure and effective. Hence face recognition becomes one of the best technologies in the biometrics field. Although plenty of efforts have been carried out in the field of face recognition, this work has mark with many challenges in general settings. The criteria behind the successful face recognition systems are developed only under constrained situations with small sized data bases. Hence there is necessary to work under general setting that fit to big data for face recognition. This paper, aims to increase performance and accuracy of face recognition by considering face spoofing attack (veracity). Result analysis of our proposed work ensures increase in accuracy compared to other methods.

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.

Although researchers have achieved a relatively high accuracy in face recognition, pose variations are still a big problem in the real life. This paper proposes a novel Generative Adversarial Network with global and local discriminators for frontal view synthesis. Two discriminators are designed to deal with global features and local pixels respectively. This paper also introduces a combined loss function with L1 loss, blur loss, and identity preserving loss. The method is effective for facial analysis tasks with synthesized faces. Experiments show the method can preserve both identities and image quality.

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

The application scenarios of face recognition are very wide, and it has become a research hotspot in the field of computer vision. The deep learning technology represented by the convolutional neural network has shown great advantages in the field of face recognition. Its network structure is developing in a deeper and wider direction, and has achieved better and better recognition results. Due to its complex structure, the training is difficult to converge and occupies a large amount of memory and takes a long time to load, which is not conducive to the application in practical engineering. In order to solve this problem, this paper studies the application of deep learning in the field of face recognition, and proposes an improved residual neural network. On the basis of the original model, this model sacrifices less accuracy, and improves the speed of face recognition to a greater extent. This makes the model of great significance in the field of engineering practice.

This paper presents a face and posture recognition model using convolutional neural network for adapting different resolution images. The performance of face recognition has dramatically improved by applying deep learning. However, it is known that the accuracy of face recognition would be decreased according to the resolution of face image. This problem is not negligible for some applications such as the real-time face recognition by surveillance camera, since the camera cannot always capture a face in a suitable position and distance. In order to solve this problem, our model adapts the resolution of an input image by selecting a proper model according to the resolution. In the experiment, we confirm the relationships between the recognition accuracy of face and posture and the resolution of captured image.

In order to realize high-accuracy recognition of aerobics actions, a highly applicable deep learning model and faster data processing methods are required. Therefore, it is a major difficulty in the field of research on aerobics action recognition. Based on this, this paper studies the application of the convolution neural network (CNN) model combined with the pyramid algorithm in aerobics action recognition. Firstly, the basic architecture of the convolution neural network model based on the pyramid algorithm is proposed. Combined with the application strategy of the common recognition model in aerobics action recognition, the traditional aerobics action capture information is processed. Through the characteristics of different aerobics actions, different accurate recognition is realized, and then, the error of the recognition model is evaluated. Secondly, the composite recognition function of the convolution neural network model in this application is constructed, and the common data layer effect recognition method is used in the optimization recognition. Aiming at the shortcomings of the composite recognition function, the pyramid algorithm is used to improve the convolution neural network recognition model by deep learning optimization. Finally, through the effectiveness comparison experiment, the results show that the convolution neural network model based on the pyramid algorithm is more efficient than the conventional recognition method in aerobics action recognition.

Convolutional neural networks (CNN) applications range from recognition and reasoning (such as handwriting recognition, facial expression recognition and video surveillance) to intelligent text applications such as semantic text analysis and natural language processing applications. Two key observations drive the design of a new architecture for CNN. First, CNN workloads exhibit a widely varying mix of three types of parallelism: parallelism within a convolution operation, intra-output parallelism where multiple input sources (features) are combined to create a single output, and inter-output parallelism where multiple, independent outputs (features) are computed simultaneously. Workloads differ significantly across different CNN applications, and across different layers of a CNN. Second, the number of processing elements in an architecture continues to scale (as per Moore's law) much faster than off-chip memory bandwidth (or pin-count) of chips. Based on these two observations, we show that for a given number of processing elements and off-chip memory bandwidth, a new CNN hardware architecture that dynamically configures the hardware on-the-fly to match the specific mix of parallelism in a given workload gives the best throughput performance. Our CNN compiler automatically translates high abstraction network specification into a parallel microprogram (a sequence of low-level VLIW instructions) that is mapped, scheduled and executed by the coprocessor. Compared to a 2.3 GHz quad-core, dual socket Intel Xeon, 1.35 GHz C870 GPU, and a 200 MHz FPGA implementation, our 120 MHz dynamically configurable architecture is 4x to 8x faster. This is the first CNN architecture to achieve real-time video stream processing (25 to 30 frames per second) on a wide range of object detection and recognition tasks.

Facial recognition technology has advanced significantly due to the development of deep learning algorithms. This paper explores deep learning, a branch of machine learning that employs multi-layered neural networks to simulate human decision-making processes in facial recognition. It provides a brief literature review of significant works in various deep learning architectures, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The core of the study is the implementation of the GhostFaceNets model, an enhancement of GhostNets, which is specifically designed for efficient and accurate facial recognition. By using Ghost Modules, this model reduces computational redundancy in generating additional feature maps through linear operations. An integrated attention mechanism is used in this study to emphasize critical facial features. Additionally, this study also employs the ArcFace loss function to improve class separation accuracy. The VGG2-FP dataset was used to train and evaluate this model and achieved an accuracy of 94.45%. This study contributes to the evolution of facial recognition systems, particularly in constrained computational environments.

PurposeThe aim of this study was to analyse and review deep learning convolutional neural networks for detecting and diagnosing early-stage dental caries on periapical radiographs.Materials and MethodsIn order to conduct this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Studies published from 2015 to 2021 under the keywords (deep convolutional neural network) AND (caries), (deep learning caries) AND (convolutional neural network) AND (caries) were systematically reviewed.ResultsWhen dental caries is improperly diagnosed, the lesion may eventually invade the enamel, dentin, and pulp tissue, leading to loss of tooth function. Rapid and precise detection and diagnosis are vital for implementing appropriate prevention and treatment of dental caries. Radiography and intraoral images are considered to play a vital role in detecting dental caries; nevertheless, studies have shown that 20% of suspicious areas are mistakenly diagnosed as dental caries using this technique; hence, diagnosis via radiography alone without an objective assessment is inaccurate. Identifying caries with a deep convolutional neural network-based detector enables the operator to distinguish changes in the location and morphological features of dental caries lesions. Deep learning algorithms have broader and more profound layers and are continually being developed, remarkably enhancing their precision in detecting and segmenting objects.ConclusionClinical applications of deep learning convolutional neural networks in the dental field have shown significant accuracy in detecting and diagnosing dental caries, and these models hold promise in supporting dental practitioners to improve patient outcomes.

In recent years, the performances of face recognition have been improved significantly by using convolution neural networks (CNN) as the feature extractors. On the other hands, in order to avoid spreading COVID-19 virus, people would wear mask even when they want to pass the face recognition system. Thus, it is necessary to improve the performance of masked face recognition so that users could utilize face recognition methods more easily. In this paper, we propose a feature extraction backbone named ResSaNet that integrates CNN (especially Residual block) and Self-attention module into the same network. By capturing the local and global information of face area simultaneously, our proposed ResSaNet could achieve promising results on both masked and non-masked testing data.