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Deep Learning-Powered Face recognition Attendance System: A Novel Approach to Automated Prediction 2

Deep Learning-Powered Face Recognition Attendance System: A ...

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2nd international conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Sornalakshmi, M. Abinash, M.J. Gopalakrishnan, S. Kasthuri, R. Nivedhitha Harini, C.M. Balamurugan, K. Department of Computer Science PG Arulmigu Kalasalingam College of Arts and Science Tamilnadu India Arulmigu Kalasalingam College of Arts and Science Tamilnadu India

ISBN: (纸本)9798350340235

Taking attendance is one of the most critical activities that must be completed on a regular basis at colleges, universities, organizations, schools, and companies. The bulk of the time, it is done manually, such as by name or roll number. The primary purpose of this work is to develop a Face recognition-based attendance system that will automate this human procedure. This research work fits the standards for modernizing the way attendance is handled, as well as the time management criteria. This gadget is put in the classroom and is programmed with student information such as name, roll number, class, sec, and photos. Open Computer Vision (CV) is used to extract the photos. The understudy can move toward the machine before the beginning of the connected example, and it will start taking photos and contrasting them with the passing dataset. The camera and processor board in this task were a Logitech C270 web camera and a NVIDIA Jetson Nano Engineer pack. The image is changed as follows: Initial, a Haar Cascade classifier is utilized to recognize faces, trailed by the LBPH (Neighborhood Twofold Example Histogram) Calculation, histogram information is considered in contrast to a preset dataset, and the gadget consequently distinguishes participation. A Succeed document is made and refreshed consistently with data from the pertinent class educator. The accompanying work summarizes the fundamental elements of a facial recognition -based attendance system. © 2023 IEEE.

关键词： Face recognition Local Binary pattern Histogram Open Computer Vision

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Oracle Bone Script recognition Based on Multi-scale Feature Fusion and Knowledge Distillation 27th

Oracle Bone Script Recognition Based on Multi-scale Feature ...

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27th international conference on pattern recognition, ICPR 2024

作者： Wang, Jiaoyan Xu, Xuebin Aysa, Alimjan Ding, Jun Ubul, Kurban School of Computer Science and Technology Xinjiang University Ürümqi830046 China Xinjiang Multilingual Information Technology Key Laboratory Xinjiang University Ürümqi830046 China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Xinjiang University Ürümqi830046 China

ISBN: (纸本)9783031783111

Oracle bone scripts, being the oldest and most developed writing system discovered in China till date, have been meticulously studied by numerous scholars. This paper presents the Multi–scale Feature Fusion Attention Net (MFFA–Net) as a solution to the issue of variant characters in the oracle bone affecting recognition accuracy. The network is based on ResNet18 and employs asymmetric convolution alongside a refined coordinate attention technique to acquire image features. It also integrates perceptual field information of varying sizes through hierarchical bilinear pooling. Furthermore, knowledge from the Wide_ResNet101 and DenseNet169 is transferred to MFFA-Net using knowledge distillation techniques. Finally, to validate the effectiveness of our proposed method, we conducted rigorous experiments on the OBC306, OBC265, and EOBC datasets, comparing our results with those of existing methods. The experimental results demonstrate that our method obtains remarkable performance in oracle character recognition, reaching state-of-the-art Top–1 accuracy with 92.42%, 94.78%, and 98.82% on the OBC306, OBC265, and EOBC datasets, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Attention Feature Fusion Knowledge Distillation Oracle recognition

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User Activity recognition and pattern Prediction Using Call Data Mining

User Activity Recognition and Pattern Prediction Using Call ...

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2023 international conference on Circuit Power and computing Technologies, ICCPCT 2023

作者： Ojha, Nitish Kumar Ranjan, Preetish Amity University Dept. of CSE Noida India Amity University Dept. of CSE Bihar Patna India

ISBN: (纸本)9798350333244

The story of technology is certainly as old as humanity itself. With the advancement of time, human society has been instrumental in communication technology. Technology has worked in two ways, as a supporting tool for crime as well as a helping tool to identify and investigate the e at the same time. In the last decade, India has seen rapid growth in the field of mobile communication as it is continuously increasing at an exponential rate, the availability of cheap data and availability of economical rate of calling encourages users to make calls easily and this continuous calling within a certain group creates a network considering relationship a parameter. The use of existing models of rapid data mining on mobile phone call logs / call data records (CDR)establishes a new dimension in the field of a user's social behavior. Meanwhile, it makes a possible methodology of suspect detection based on certain attributes i.e., cell phone conversation ratio, calling pattern among users, terrain-basedfacts, etc. In this paper, key areas which have been covered are 'Machine's perception towards prediction of complex social systems and strange behavior of cell phone user' based on data mining techniques applied on call details of cell phone users. © 2023 IEEE.

关键词： Forecasting

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Revisiting the Benefits of Knowledge Distillation against Adversarial Examples 6

Revisiting the Benefits of Knowledge Distillation against Ad...

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6th international conference on Data Science and Information Technology, DSIT 2023

作者： Li, Jiawen Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350304442

Deep neural networks (DNNs) have always been a popular base model in many image classification tasks. However, some recent works suggest that there are some man made images will easily lead DNNs give wrong predictions. These images are generated by adding some invisible perturbations into clean images, which are the so-called adversarial examples. Since they are almost similar to original data but can fool DNNs, adversarial examples can bring the insecurity to many life-critical applications. Adversarial knowledge distillation is a positive defense method to attach improved robustness to the target DNNs against adversarial examples. In distillation, student models are trained by soft labels coming from robust pre-trained teacher networks. However, it is hard for researchers to ensure that supervised information comes from a better teacher model. In this paper, We focus on four potential factors of the teacher that may have an effect to the distillation performance of student model, and investigate whether and when they can boost the model robustness against adversarial attacks. Multiple quantitative experiment illustrate that these factors have impact on the distillation process and these results can be considered to choose a better teacher model. Further studies can focus on the combination of adversarial knowledge distillation and other defense schemes. Empirical results in this paper may also help select proper teacher in new complex distillation tasks. © 2023 IEEE.

关键词： Distillation

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An LSTM-based Framework for Motion pattern recognition of Handling Robots 7

An LSTM-based Framework for Motion Pattern Recognition of Ha...

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7th international conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Li, Fangyu Zheng, Jianbin Yan, Zhiqi Tang, Yong Chen, Liufei Wang, Zhuorui Gao, Yifan Wang, Yu Wuhan University of Technology School of Information Engineering Wuhan China Third Construction Co. Ltd. China Construction Third Engineering Bureau Wuhan China Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan China

ISBN: (纸本)9781665478571

With the development of the construction industry, construction work more and more tends to mechanization, automation, and intelligence. The appearance of handling robots greatly improves the handling efficiency and reduces the cost. Even though great efforts have been made on motion pattern recognition for handling robots, the existing methods still have some shortcomings. Previous studies are all used for motion pattern recognition, each input is processed separately, which cuts off the time continuity between the latter data and the former data. To this end, in this paper, we first emphasize the importance of temporal correlation between data in the field of motion pattern recognition and devise an LSTM based method to ease the issue above. Finally, we use softmax to effectively calculate the classification results of data potential features and temporal information. Compared with traditional machine learning models and popular neural network models, extensive experiments on real-world datasets demonstrate the effect of our framework is superior to the baselines, thus providing novel perspectives to the building automation industry. © 2022 IEEE.

关键词： Long short-term memory

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Implementing Open Source Biometric Face Authentication for Multi-factor Authentication Procedures 11th

Implementing Open Source Biometric Face Authentication for M...

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11th international conference on High-Performance computing Systems and Technologies in Scientific Research, Automation of Control and Production (HPCST)

作者： Minakova, Natalya Mansurov, Alexander Altai State Univ Lenin Ave 61 Barnaul 656049 Russia

ISBN: (纸本)9783030941413;9783030941406

This study proposes a solution that extends the capabilities of web information systems with single-factor authentication by introducing an additional authentication factor based on biometric face recognition. The proposed solution design and its main operation steps are presented and discussed. The solution utilizes the standard multimedia functionality of popular web browsers and supports available or built-in image capturing devices (photo and web cameras). Robust program algorithms from the open source computer vision library are used for face image processing and analysis. Experimental testing and validation of the algorithms for face localization and recognition are conducted with image sets produced with consideration of reality. Experimental results demonstrate high effectiveness with a success rate of 80% ... 93% for the solution based on the local binary pattern face localization algorithm with the local binary pattern histogram face recognition algorithm.

关键词： Biometrics Face authentication Face recognition Computer vision Local binary pattern

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DSWHAR: A Dynamic Sliding Window Based Human Activity recognition Method 19

DSWHAR: A Dynamic Sliding Window Based Human Activity Recogn...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Sun, Li Yang, Xiaodong Hu, Chunyu School of Computer Science and Technology Jinan250353 China Shandong Academy of Intelligent Computing Technology Jinan250101 China Institute of Computing Technology Chinese Academy of Sciences The Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing100190 China

ISBN: (纸本)9798350346558

Sensory data has been widely used for human activity recognition (HAR), where sliding window (SW) is one of the typical methods to segment continuous signals. Most existing HAR methods select fixed-length sliding windows for different activities. However, with complex and diverse human activities, a single fixed sliding window is not suitable for acquiring features of different activities. Some studies have dynamically adjusted the window size based on the activity of feature difference segmentation, but the continuous occurrence of the activity can lead to an oversized window for segmentation and easily introduce additional noise leading to degradation of classification performance. To tackle the above problem, we propose a novel dynamic sliding window based human activity recognition method, called DSWHAR. In the training phase, DSWHAR firstly explores the relationship between the optimal sliding window length and the signal spectrum of each target activity, then a candidate window set is built for the target activity and multiple classifiers are trained using different windows. During the recognition phase, the latest prediction probability is used to dynamically select a suitable window for the next prediction, and the corresponding classifier is used for the final recognition. We compare DSWHAR with the method with fixed sliding windows and dynamic sliding window algorithm on the MHEALTH dataset, respectively, and the experimental results show that DSWHAR not only achieves better overall HAR accuracy but also has unbiased performance on different activities. © 2022 IEEE.

关键词： pattern recognition

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Solving Numerical Puzzles and Sudoku using pattern recognition and Image Processing 22

Solving Numerical Puzzles and Sudoku using Pattern Recogniti...

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2nd international conference on computing Advancements: Age of computing and Augmented Life, ICCA 2022

作者： Akhter, Shanjidah Sazzad, Mehedi Hasan Asif, Samir Department of Computer Science American International University-Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (纸本)9781450397346

Puzzles are a useful means to test an individual's critical reasoning and ingenuity. In a puzzle, to arrive at the solution one must follow a precise logical way abiding by certain pre-defined rules. Puzzles are not only a mere form of entertainment, but it also contributes significantly to advanced areas of computational research and intelligent system design and testing. Solving puzzles often require pattern recognition in a logical sequence. Puzzles based on numerical computations may provide a platform to evaluate an algorithm's performance. Among various types of numerical puzzles, Sudoku is a popular variant that is used and analyzed a lot in mathematical and computational research and algorithm designing. In this work, a 9×9 puzzle grid is studied, and its solution is generated using backtracking. Then the patterns and line segments of the puzzle grid are recognized, and all the intersection points are detected. Finally, the row images are extracted and the digits from the images are detected by comparing them with template data using pattern recognition and image processing techniques using MATLAB. © 2022 ACM.

关键词： Image processing

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A Systematic Review on the Identification and Classification of patterns in Microservices 4

A Systematic Review on the Identification and Classification...

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4th international conference for Emerging Technology, INCET 2023

作者： Norton Stanley, S.A. Sebastian, Shoney Department of Computer Science Bengaluru India

ISBN: (纸本)9798350335750

Determining patterns in monolithic systems to help improve the overall system development and maintenance has become quite commonplace. However, recognizing the patterns that have emerged (or are emerging) in cloud computing - especially with respect to microservices, is challenging. Although numerous patterns have been proposed through extensive research and implementation, the quality assessment tools that are currently available fall short when it comes to accurately recognizing patterns in microservices. It has been identified that a completely autonomous tool for the identification and classification of patterns in microservices has not been developed so far. Moreover, classification of services is an approach that has not been considered by researchers that are working in this field. This paper aims to perform a detailed systematic literature review that can help to explore the various possibilities of identifying and classifying the patterns in microservices. The article also briefly lists out a set of tools that is used in the industry for the implementation of patterns in microservices. © 2023 IEEE.

关键词： pattern recognition

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FoCLBP+2DGF: Fusion of Complete Local Binary pattern with 2D Geometry Features for Facial Emotion recognition in Videos 1

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19th international conference of Intelligent Unmanned Systems, ICIUS 2023

作者： Singh, Lovejit Singh, Sarbjeet Aggarwal, Naveen Singh, Ranjit Department of Computer Science and Engineering Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering Panjab University Chandigarh160014 India

ISBN: (数字)9789819765911

ISBN: (纸本)9789819765904

People have different facial attributes in terms of their skin color, shape, and size. This diversity makes a challenge in the development of generalized automatic intelligent system for human’s emotions recognition through their facial expressions. Over the decades, researchers have significantly contributed to geometry and texture-based methods to act for facial contents in image/video in the form of feature vector. This paper presents a fusion of texture based complete local binary pattern (CLBP) method with two-dimensional (2D) geometry features (FoCLBP+2DGF) method to represent the facial expressions for emotion recognition in videos. The proposed method better describes the contrast and structural behavior of facial contents in spatial domain with fusion of CLBP and 2D geometry feature computing. It aggregates the facial expression’s behavior in temporal domain by sum normalization method to capture ratio among the features. The constructed final feature vector with proposed method is independent from the duration of video. Further, Bayesian optimization method is used to identify the best hyperparameters of multiclass support vector machines (SVMs) with proposed FoCLBP+2DGF method. Experimental results show that the proposed FoCLBP+2DGF method attained ≈ 88.64 ± 0.38% emotion recognition accuracy with SVMs using Gaussian kernel function in videos of crowd-sourced emotional multimodal actors’ dataset (CREMA-D). The proposed method significantly improved accuracy of intelligent system for human emotion recognition in videos over the human accuracy and existing work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intelligent systems

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