Custom Deep Learning Face Recognition based on Tensorflow Model for Attendance Management System

Face recognition is widely used as identity authentication method in the Internet of Things (IoTs). However, compared with static camera, the background of IoT devices may change. Additionally, the facial expression or posture may change as well. Although face recognition has been researched for several decades, it is still a challenging task to design a robust face recognition system in IoT environment. This paper designs a special face recognition system by introducing deep transfer learning to adapt difference scenes in IoT environment. The face recognition system consists of two parts: terminal smart devices and cloud center. The terminal smart device consists of a camera to capture face picture, a wireless network part to communicate with cloud center, and a screen to show recognition result. The cloud center receives the face picture from terminal smart device and returns the face recognition result by using an offline deep transfer learning model. Compared with previous models, deep transfer learning model can better adapt different scenes. The experiments shows that the proposed face recognition system achieves more promising results than two source‐only models and two shallow transfer learning models.

Due to the current manual recording of students' classroom attendance by teachers, it is not conducive to later data statistics and analysis. In response to the attendance issue of such students in class, this article proposes a facial recognition algorithm based on FaceNet, which detects targets using multi-scale convolutional feature layers and is suitable for fast object detection; FaceNet maps faces to Euclidean space through deep learning and uses the distance between feature vectors to determine face similarity. Subsequently, a student classroom attendance management system was designed, integrating information collection, facial recognition, random roll call, and data management modules. FaceNet algorithm was used for facial feature extraction and recognition. The experimental results show that the system can effectively achieve classroom attendance management, but attention should be paid to the impact of camera resolution and face size on recognition accuracy. This method ultimately achieved the goal of recording students' classroom attendance, thus achieving the purpose of student classroom management.

As we all know fingerprint recognition is one of the secure and accurate Biometric Technologies. If think about it in deep even with the Biometric system the virus can be spread during these situations. To overcome this, we need to come up with a secure and contactless way of authentication. So, let’s update to some contactless remedies like Iris authentication which are unique for every individual and they don’t need to have any physical contact. So, we can use this Iris detection for a secure and contactless authentication system. The main aim of this research is to provide contactless remedies for students in Educational institutes like Smart Locking system, Attendance management system, and Library Transaction by using their Iris authentication and Face Recognition. Coming to the outline of the attendance management system, we will first collect the data from the Kaggle repository. Next, we split the data into training and testing, then we will train the data using transfer learning techniques and test the model by using test data. Finally, we integrated the trained model with the flask. If the Iris matches then the attendance of a particular person will be posted. If not matched then we train the model by adding new person’s data. For the construction of modern electronic security systems, real-time face recognition is crucial. Face detection, feature extraction, and face recognition are the three procedures involved. After recognizing the face, it will check whether the person’s face matches the collected database. If it matches it will show the person’s name, the number of books he took, and what those books are for Library transactions and in the same way the locker will be open if the person’s data is matched. The proposed methods are secure and unique contactless ways of authentication for every individual. So, we can use these detection and authentication systems for secure and contactless applications. It can be successfully used for students in Educational institutes like Smart Locking system, Attendance management system, and Library Transaction by using their Iris authentication and Face Recognition. The Covid-19 infection in society will undoubtedly decline if the proposed argument is implemented.

Abstract— In this digital era, face recognition system plays a vital role in almost every sector. Face recognition is one of the mostly used biometrics. It can used for security, authentication, identification, and has got many more advantages. Despite of having low accuracy when compared to iris recognition and fingerprint recognition, it is being widely used due to its contactless and non-invasive process. Furthermore, face recognition system can also be used for attendance marking in schools, colleges, offices, etc. This system aims to build a class attendance system which uses the concept of face recognition as existing manual attendance system is time consuming and cumbersome to maintain. And there may be chances of proxy attendance. Thus, the need for this system increases. This system consists of four phases- database creation, face detection, face recognition, attendance updation. Database is created by the images of the students in class. Face detection and recognition is performed using Haar-Cascade classifier and Local Binary Pattern Histogram algorithm respectively. Faces are detected and recognized from live streaming video of the classroom. Keywords—Face Recognition; Face Detection; Haar-Cascade; Opencv :library are used in face recognition system.

Face recognition is among the most productive image processing applications and has a pivotal role in the technical field. In the recent time automated face recognition has become a trend and has been developed very much, this is mainly due to two reasons first it is due to availability of modern technologies and second is due to the ability to save time using face recognition in the process of taking attendance of students. Its usage will grow vast in the future as it saves a lot of time. It consumes a lot of time to take attendance manually and few might also fake the attendance, in order to prevent time consumption and avoid faking the attendance. Face recognition is used to identify the person present in the class and mark their attendance , this is done with the help of image or video frame. We proposed an automatic attendance management system using techniques such as PCA algorithm. The face detection and recognition will automatically detect the students in the classroom and mark the attendance by recognizing the person. The traditional process of making attendance and present biometric systems are vulnerable to proxies. This paper is therefore proposed to tackle all these problems. The proposed system makes the use of Haar classifiers, KNN, CNN, SVM, Generative adversarial networks, and Gabor filters. After face recognition attendance reports will be generated and stored in excel format. The system is tested under various conditions like illumination, head movements, the variation of distance between the student and cameras. After vigorous testing overall complexity and accuracy are calculated. The Proposed system proved to be an efficient and robust device for taking attendance in a classroom without any time consumption and manual work. The system developed is cost-efficient and need less installation

The three main modules of autonomous vehicles, i.e., sensing, decision making, and motion controlling, have been studied separately in most existing works on autonomous driving, which overlook the correlations among these modules, leading to a result of unsatisfactory performance. In this paper, we propose a novel scheme that first tactfully processes the sensing data, then jointly learns and optimizes the decision-making and motion-controlling using reinforcement learning (RL). Specifically, the proposed scheme designs a novel state representation mechanism, where the sensing data goes through the attention layer and the convolutional neural network (CNN) layer sequentially. The attention layer focuses on extracting the most important local information and then CNN layer takes a broad view to comprehensively consider the global information for a better representation. Furthermore, the proposed scheme jointly learns decision-making and motion-controlling, therefore, the relevance of these two modules is implicitly considered, which helps to achieve a better autonomous driving policy. Extensive simulation results show that the proposed scheme is better than classic control methods and some RL methods in terms of safety, velocity, etc. We also demonstrate the respective functions of the attention layer and the CNN layer through ablation studies. Finally, we construct a traffic scene with a real autonomous vehicle, and verified the feasibility of the proposed scheme.

Lightweight deep learning models for face recognition are becoming increasingly crucial for deployment on resource‐constrained devices such as embedded systems or mobile devices. This paper presents a highly efficient and compact deep learning (DL) model that achieves state‐of‐the‐art performance on various face recognition benchmarks. The developed DL model employs one‐ or few‐shot learning to obtain effective feature embeddings and draws inspiration from FaceNet with significant refinements to achieve a memory size of only 3.5 MB—about 30 times smaller than FaceNet—while maintaining high accuracy and real‐time performance. The study demonstrates the model's effectiveness through extensive experiments, which include testing on public datasets and the model's ability to recognize occluded faces in uncontrolled environments using grayscale input images. Compared to the state‐of‐the‐art lightweight models, the proposed model requires fewer FLOPs (0.06G), has a smaller number of parameters (1.2 M), and occupies a smaller model size (3.5 MB) while achieving a competitive level of recognition accuracy and real‐time performance. The results show that the model is well‐suited for deployment in embedded domains, including live entrance security checks, driver authorization, and in‐class attendance systems. The entire code of FN8 is available on GitHub .

An end-to-end deep learning approach to MI-EEG signal classification for BCIs

A facial recognition system is a piece of technology that can match a human face from a database of images. It is often used for robust authentication. An Attendance Management System is a tool used by businesses to track employee time and participation information. Software as a service (SaaS) allows users to access the software via the Internet as a service. Advanced and automated attendance management technologies are being used to address this issue. The problem of proxies and students being counted as present even if they are not is easily resolved with this architecture. This technology records attendance via a live video broadcast. This system's objective is to develop a facial recognition-based class attendance system because the manual technique of taking attendance currently in use is cumbersome and difficult to maintain. The creation of the database, face detection, face recognition, and attendance updating are the four stages of this system. Faces can be recognized in the live-streamed video from the classroom. The attendance will be recorded in a spreadsheet and saved to Google Drive at the end of the session.

Research in many fields has shown that transfer learning (TL) is well-suited to improve the performance of deep learning (DL) models in datasets with small numbers of samples. This empirical success has triggered interest in the application of TL to cognitive decoding analyses with functional neuroimaging data. Here, we systematically evaluate TL for the application of DL models to the decoding of cognitive states (e.g., viewing images of faces or houses) from whole-brain functional Magnetic Resonance Imaging (fMRI) data. We first pre-train two DL architectures on a large, public fMRI dataset and subsequently evaluate their performance in an independent experimental task and a fully independent dataset. The pre-trained DL models consistently achieve higher decoding accuracies and generally require less training time and data than model variants that were not pre-trained, while also outperforming linear baseline models trained from scratch, clearly underlining the benefits of pre-training. We demonstrate that these benefits arise from the ability of the pre-trained models to reuse many of their learned features when training with new data, providing deeper insights into the mechanisms giving rise to the benefits of pre-training. Yet, we also surface nuanced challenges for whole-brain cognitive decoding with DL models when interpreting the decoding decisions of the pre-trained models, as these have learned to utilize the fMRI data in unforeseen and counterintuitive ways to identify individual cognitive states.

Nowadays, the fourth industrial revolution has achieved significant advancement in high technology, in which artificial intelligence has had vigorous development. In practice, facial recognition is one most essential tasks in the field of computer vision with various potential applications from security and attendance system to intelligent services. In this paper, we propose an efficient deep learning approach to facial recognition. The paper utilizes the architecture of improved FaceNet model based on MobileNetV2 backbone with SSD subsection. The improved architecture uses depth-wise separable convolution to reduce the model size and computational volume and achieve high accuracy and processing speed. To solve the problem of identifying a person entering and exiting an area and integrating on advanced mobile devices limits to (such as limited memory and on-device storage) highly mobile resources. Especially, our approach yields better results in practical application with more than 95% accuracy on a small dataset of the original face images. Obtained frame rate (25 FPS) is very favorable compared to the field of facial recognition using neural network. Besides, the deep learning based on solution could be applicable in many low-capacity hardware or optimize system’s resource. Finally, the smart automated attendance systems is successfully designed basing on the improved efficient facial recognition.

The past few years have seen a surge of applying Deep Learning (DL) models for a wide array of tasks such as image classification, object detection, machine translation, etc. While DL models provide an opportunity to solve otherwise intractable tasks, their adoption relies on them being optimized to meet latency and resource requirements. Benchmarking is a key step in this process but has been hampered in part due to the lack of representative and up-to-date benchmarking suites. This is exacerbated by the fast-evolving pace of DL models. This paper proposes DLBricks, a composable benchmark generation design that reduces the effort of developing, maintaining, and running DL benchmarks on CPUs. DLBricks decomposes DL models into a set of unique runnable networks and constructs the original model's performance using the performance of the generated benchmarks. DLBricks leverages two key observations: DL layers are the performance building blocks of DL models and layers are extensively repeated within and across DL models. Since benchmarks are generated automatically and the benchmarking time is minimized, DLBricks can keep up-to-date with the latest proposed models, relieving the pressure of selecting representative DL models. Moreover, DLBricks allows users to represent proprietary models within benchmark suites. We evaluate DLBricks using $50$ MXNet models spanning $5$ DL tasks on $4$ representative CPU systems. We show that DLBricks provides an accurate performance estimate for the DL models and reduces the benchmarking time across systems (e.g. within $95\%$ accuracy and up to $4.4\times$ benchmarking time speedup on Amazon EC2 c5.xlarge).

The automatic recognition of human faces presents a significant challenge to the pattern recognition research community. Typically, human faces are very similar in structure with minor differences from person to person. They are actually within one class of "human face". Furthermore, lighting conditions change, while facial expressions and pose variations further complicate the face recognition task as one of the difficult problems in pattern analysis. This paper proposes a novel concept: namely, that faces can be recognized using a line edge map (LEM). The LEM, a compact face feature, is generated for face coding and recognition. A thorough investigation of the proposed concept is conducted which covers all aspects of human face recognition, i.e. face recognition under (1) controlled/ideal conditions and size variations, (2) varying lighting conditions, (3) varying facial expressions, and (4) varying pose. The system performance is also compared with the eigenface method, one of the best face recognition techniques, and with reported experimental results of other methods. A face pre-filtering technique is proposed to speed up the search process. It is a very encouraging to find that the proposed face recognition technique has performed better than the eigenface method in most of the comparison experiments. This research demonstrates that the LEM, together with the proposed generic line-segment Hausdorff distance measure, provides a new method for face coding and recognition.

Assessing human stress in agriculture proves to be a complex and time‐intensive endeavor within the field of ergonomics, particularly for the development of agricultural systems. This methodology involves the utilization of instrumentation and the establishment of a dedicated laboratory setup. The complexity arises from the need to capture and analyze various physiological and psychological indicators, such as heart rate (HR), muscle activity, and subjective feedback to comprehensively assess the impact of farm operations on subjects. The instrumentation typically includes wearable devices, sensors, and monitoring equipment to gather real‐time data of subject during the performance of farm operations. Deep learning (DL) models currently achieve human performance levels on real‐world face recognition tasks. In this study, we went beyond face recognition and experimented with the recognition of human stress based on facial features during the drudgery‐prone agricultural operation of sugarcane harvesting. This is the first research study for deploying artificial intelligence‐driven DL techniques to identify human stress in agriculture instead of monitoring several ergonomic characteristics. A total of 20 (10 each for male and female) subjects comprising 4300 augmented RGB images (215 per subject) were acquired during sugarcane harvesting seasons and then these images were deployed for training (80%) and validation (20%). Human stress and nonstress states were determined based on four ergonomic physiological parameters: heart rate (ΔHR), oxygen consumption rate (OCR), energy expenditure rate (EER), and acceptable workload (AWL). Stress was defined when ΔHR, OCR, EER, and AWL reached or exceeded certain standard threshold values. Four convolutional neural network‐based DL models (1) DarkNet53, (2) InceptionV3, (3) MobileNetV2 and (4) ResNet50 were selected due to their remarkable feature extraction abilities, simple and effective implementation to edge computation devices. In all four DL models, training performance results delivered training accuracy ranging from 73.8% to 99.1% at combinations of two mini‐batch sizes and four levels of epochs. The maximum training accuracies were 99.1%, 99.0%, 97.7%, and 95.4% at the combination of mini‐batch size 16 and 25 epochs for DarkNet53, InceptionV3, ResNet50, and MobileNetV2, respectively. Due to the best performance, DarkNet53 was tested further on an independent data set of 100 images and found 89.8%–93.3% confident to classify stressed images for female subjects while 92.2%–94.5% for male subjects, though it was trained on the integrated data set. The comparative classification of the developed model and ergonomic measurements for stress classification was carried out with a net accuracy of 88% where there were few instances of wrong classifications.

The main idea and the driver of further research in the area of face recognition are security applications and human-computer interaction. Face recognition represents an intuitive and non-intrusive method of recognizing people and this is why it became one of three identification methods used in e-passports and a biometric of choice for many other security applications. This goal of this book is to provide the reader with the most up to date research performed in automatic face recognition. The chapters presented use innovative approaches to deal with a wide variety of unsolved issues.