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Visual Recognized Attendance System 2

Visual Recognized Attendance System

引用

2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Chitra, N. Thulasi Sreenivasulu, G. Subhashini, P. Tripathy, Hrudaya Kumar Mlr Institute of Technology Department of Computer Science and Information Technology India Vardhaman College of Engineering Department of Cse Shamshabad India Mlr Institute of Technology Department of Computer Science and Information Technology Hyderabad India Kalinga Institute of Industrial Technology Deemed to Be University India

ISBN: (纸本)9798350370188

Usually in our regular class we face a situation where faculty or respective administrators take attendance which would consume time. So, this project aims to develop a visual recognized attendance register that will identify the people based on the video and mark as present in the register. The data set is created by taking pictures of the people that are in a particular class. Based on the pictures the model will develop a identification mark or some important patterns for each student, based on this when we start a web cam through our model it will identify people and post attendance. We have local binary pattern histogram and convolutional neural network to identify each person uniquely. And to develop this we use OpenCV library which contains which contains many features regarding face recognition. © 2024 IEEE.

关键词： local binary pattern

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Scene-Based Event Detection using Grey Wolf Optimization Enabled BiLSTM Classification 2

Scene-Based Event Detection using Grey Wolf Optimization Ena...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Sanas, Shrikant P. Sarode, Tanuja Tsec Mumbai Bandra West Maharashtra Mumbai400050 India Tsec Mumbai India

ISBN: (纸本)9798350368413

Event detection is a crucial task that enables analyzing the rapid moment of events for rectifying the issues associated with the event that occurred. Despite, several traditional methods have been developed for event detection, the methods lag in identifying the effective localization of key moments and the classification of localized key moments. To address the aforementioned issues and to differentiate the events that contain similar objects or scenes, a novel framework is developed utilizing the Bi-directional Long Short Term Memory with Grey Wolf Optimization (BiLSTM-GWO) for efficient feature extraction and classification. The novel BiLSTM with GWO framework in which the features including the Scale Invariant Feature Transform(SIFT), Hybrid ternary pattern involving the local directional pattern(LDP), local binary pattern (LBP), and local optimal-oriented pattern (LOOP), and Resnet 101 are extracted to assist in learning the relationship between the scenes. For effective event detection, the BiLSTM utilizing the long-term dependency is used and the importance of the developed model relies on the GWO utilizing the encircling and hunting strategies that boost the performance of the classifier. The efficiency of the BiLSTM with GWO approach is evaluated as 91.155%, 91.7%, and 91.505% for sensitivity, specificity, and accuracy respectively with other traditional techniques concerning the USED dataset. © 2024 IEEE.

关键词： local binary pattern

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Differential Shrinkage Extended binary pattern for Texture Classification 24

Differential Shrinkage Extended Binary Pattern for Texture C...

引用

2024 International Conference on Virtual Reality, Image and Signal Processing, ICVISP 2024

作者： Xu, Xiaochun Li, Jingxin School of Computer and Big Data Minjiang University Fuzhou China International Digital Economy College Minjiang University Fuzhou China

ISBN: (纸本)9798400710926

binary pattern methods have demonstrated remarkable efficacy over the past few decades. However, most binary pattern variants have to tackle the problem of information loss during binary encoding. Even though the CLBP has proposed a relatively complete binary encoding scheme, the weak centre pattern greatly limits its classification performance. To address the issue mentioned above, this paper proposes a novel differential shrinkage extended binary pattern (DSEBP) to extract more comprehensive texture features. The DSEBP consists of three patterns: local centre shrinkage extended pattern (LCSEP), sign pattern and magnitude pattern. To get the novel LCSEP, this paper first designs a shrinkage function to obtain the centre shrinkage differential image. Then, the centre shrinkage differential image is encoded by the mean of centre shrinkage differential image to get the centre differential shrinkage patten. Finally, the original centre pattern and centre differential shrinkage patten are recoded to generate the LCSEP using multi-pattern encoding strategy. To validate the effectiveness of the DSEBP, this paper conducts a series of comparative experiments on the Outex_TC10 and Outex_TC12. The experimental results show that the proposed DSEBP has advanced classification performance. © 2024 Copyright held by the owner/author(s).

关键词： local binary pattern

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Integrating VGG16 and LBP for Accurate E-Waste Classification Towards Environmental Sustainability 3

Integrating VGG16 and LBP for Accurate E-Waste Classificatio...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Krishna, N. Hari Kumaraswamy, I. Bose, N. Subhash Chandra Sudhakar, P. Reddy, T. Ravi Sankar Krishnan, G. Hari Government Polytechnic Dept. of Eee Andhra Pradesh Kurnool India School of Engineering Mohan Babu University Dept. of Eee Andhra Pradesh Tirupati India Government Polytechnic Dept. of Eee Andhra Pradesh Rayachoti India

ISBN: (纸本)9798331509675

The growing challenge of electronic waste (e-waste) management necessitates advanced solutions for automated classification and recycling. This study presents a hybrid methodology combining transfer learning with VGG16 and handcrafted local binary patterns (LBP) features, enhanced by Principal Component Analysis (PCA) for dimensionality reduction, and classification using a Random Forest Classifier. The methodology achieves a remarkable accuracy of 98.54% on the E-Waste Image Dataset, outperforming existing approaches. Extensive evaluation across multiple metrics, including precision, recall, F1 score, and Cohen's Kappa, demonstrates the robustness of the approach. This research offers an efficient framework for sustainable e-waste classification, supporting environmental conservation efforts. © 2025 IEEE.

关键词： local binary pattern

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Optimizing Hyperparameters for an Adapted Capsule Network in Lumpy Skin Disease Diagnosis 10

Optimizing Hyperparameters for an Adapted Capsule Network in...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Mallikarjun, Goddeti Narayana, V.A. Jntuh Department of Computer Science and Engineering Hyderabad Telangana Kukatpally India Cmr College of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350384369

Lumpy Skin Disease (LSD) presents a global threat to cattle populations, with over 110,000 cattle deaths reported in India alone. The pressing need for precise diagnostic tools by exploring a Capsule Network adaptation for LSD diagnosis. The proposed model integrates advanced feature extraction layers, including Scale-Invariant Feature Transform (SIFT), local binary patterns (LBP), and Histogram of Oriented Gradients (HOG). Achieving optimal performance relies on meticulous hyperparameter tuning, with a focus on parameters such as learning rates, batch sizes, and regularization techniques, underscoring their pivotal role in maximizing diagnostic efficacy. The research delves into the intricacies of hyperparameter choices, aiming to strike a delicate balance for fine-tuning the model. The ultimate goal is to achieve optimal LSD diagnosis, contributing valuable insights to the broader field of deep learning in veterinary diagnostics. The study attains outstanding accuracy at 0.976, accompanied by balanced precision, recall, and F1 scores of 0.965, 0.971, and 0.963, respectively. The findings highlight the adapted Capsule Network's potential as a powerful tool to improve diagnostic accuracy for LSD in cattle, making noteworthy contributions to the broader field of deep learning in veterinary medicine. © 2024 IEEE.

关键词： local binary pattern

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Machine Learning Approaches for Emotion Recognition: A Comparative Study 12

Machine Learning Approaches for Emotion Recognition: A Compa...

引用

12th International Conference on Current Trends in Advanced Computing, ICCTAC 2024

作者： Singh, Himanshu Singh, Arushi Chauhan, Priyaj Kamboj, Deepika IT KIET Group of Institution Ghaziabad India CSIT KIET Group of Institution Ghaziabad India

ISBN: (纸本)9798350395808

A key component that has arisen in[10] human-computer interaction, artificial intelligence, and affective computing is the ability to recognize emotions. The objective of the research project is to examine a method that is integrated for the recognition of facial expressions of emotion. Humans transmit their emotions through indirect and non-verbal means most of the time. Their sentiments are communicated through these methods. Providing a complete overview that includes an assessment of all the important emotion detection methods that can be found in a single region is the objective of the exposition that is now being presented. The paper represents the first attempt to offer an overview of all the emotion detection models that have been developed for the previous 10 years. The document contains an in-depth analysis of the technique as well as the datasets. For face identification, the technique makes use of the local binary pattern Histogram (LBPH), Convolutional Neural Networks (CNN) for feature extraction, and the Deep Face model for comprehensive emotion analysis. © 2024 IEEE.

关键词： local binary pattern

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Ocular Disease Identification and Classification Using LBP - KNN

Ocular Disease Identification and Classification Using LBP -...

引用

2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Santhosh Kumar, B.N. Babu, G. N. K. Suresh Srishti College of Commerce and Management Department of Computer Science Bengaluru India

ISBN: (纸本)9798350359688

Significant health hazards are associated with ocular disease, and effective treatment and avoidance of irreparable damage depend on early detection. Conventional approaches frequently concentrate on specific illnesses, which results in ineffective diagnosis and treatment. This research presents a method that combines the K-Nearest Neighbours (KNN) algorithm with local binary patterns (LBP) for the diagnosis of numerous eye disorders. Proposed work is to overcome this constraint by creating a single, integrated framework that can identify several eye conditions at once. The LBP methodology is used in the proposed method to extract discriminative features from retinal pictures, efficiently capturing the texture information necessary for illness detection. These features are subsequently supplied into the KNN classifier along with various distance metrics like Seuclidean, Minkowski, and Euclidean, which uses feature vector similarity to classify images into several illness categories. In this work 99.2 accuracy and 0.979 precision has been obtained. © 2024 IEEE.

关键词： local binary pattern

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Implementation of Face Feature Algorithms for Authentication of a Person (IFFAP) 3

Implementation of Face Feature Algorithms for Authentication...

引用

3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Ramachandra, A.C. Monisha, B. Muralapur, Poorvika Vasupraneetha, H. Nitte Meenakshi Institute Of Technology Department Of Ece Bengaluru India

ISBN: (纸本)9798350318609

The present conventional digital financial transaction at Automated Teller Machines (ATM) is secured with PIN number, with this there are chances of forgetting or misusing to overcome this with the increase in technology and various advancements in financial infrastructure, most of the bank customers prefer to use Automated Teller Machines (ATM). To improve the security in such cases a new generation ATM machine which is based on face recognition technology is used. It also assists with changing passwords which increases privacy and security. The high-quality image has an important role in the recognition process. We propose Implementation of Face Feature Algorithms for Authentication of a Person (IFF AP) technique, an attempt to solve, The LBP algorithm and Haar Cascade techniques are employed for this process. This technology helps in increased security and avoids unauthorized transactions. This mutually benefits banking institutions and the customers. Leveraging existing algorithms, particularly those based on local binary pattern (LBP) and Haar Cascade, reveals a commendable qualitative analysis showcasing high accuracy up to 92.85% and improves computational efficiency compared to existing Techniques. © 2024 IEEE.

关键词： local binary pattern

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Machine Learning-based Algorithm for Type of Wood Identification 2

Machine Learning-based Algorithm for Type of Wood Identifica...

引用

2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Venugopal, D. Sandhiya, R. Swetha, S. Sredhar, R. Rohit, N. Shadrach, Finney Daniel KPR Institute of Engineering and Technology Department of ECE Coimbatore India KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798331534950

The remarkable developments in machine learning have made it feasible to use wood pictures to identify wood, a new area of research in wood science that is steadily working to the development for wood identification automatically to satisfy the demands of the market and the wood sector. There are many different tree species and the wood made from each of them has distinct structural, mechanical and physical characteristics. Due to the variations in the structure and characteristics of wood, a wide range of objects with various applications and aesthetics can be made from wood. It is necessary to employ the right species in the right places in order to use this priceless substance effectively. To help minimize misclassification of wood species in the wood industries, a novel application in the field of computer vision called intelligent wood species recognition is being researched. With the use of photographs taken of wood samples or features noticed, woods recognition implements the process of identifying the various species of wood. In this study, the LBP (local binary pattern) histogram is utilized to identify the various wood species by extracting characteristics from improved wood photos. The objective of the wood recognition system is to determine whether it is feasible to develop a system that can automatically recognize wood based on its anatomy. Due to the technique's rotation invariance and tolerance for gray scale fluctuations the result achieved thus far shows a high recognition rate and accuracy indicating that it is very promising for practical applications. © 2024 IEEE.

关键词： local binary pattern

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BLIND QUALITY ASSESSMENT OF LIGHT FIELD IMAGE BASED ON SPATIO-ANGULAR TEXTURAL VARIATION 30

BLIND QUALITY ASSESSMENT OF LIGHT FIELD IMAGE BASED ON SPATI...

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30th IEEE International Conference on Image Processing (ICIP)

作者： Zhang, Zhengyu Tian, Shishun Zou, Wenbin Zhang, Yuhang Morin, Luce Zhang, Lu Univ Rennes CNRS INSA Rennes IETR UMR 6164 F-35000 Rennes France Shenzhen Univ Coll Elect & Informat Engn Shenzhen Peoples R China

ISBN: (纸本)9781728198354

Light Field Image Quality Assessment (LF-IQA) is vitally important to facilitate the development of immersive technologies. However, current state-of-the-art LF-IQA metrics still struggle to handle Light Field Image (LFI) with massive data in an efficient manner. To cope with this challenge, we propose a simple yet effective Blind LF-IQA metric based on Spatio-Angular Textural Variation, named SATV-BLiF. Given a distorted LFI, we first apply local binary pattern (LBP) operator to measure the textural variation in the spatial and angular domains respectively. Then the generated spatial and angular textural matrices are merged and further transformed into statistical textural histogram features. Finally, Support Vector Regression (SVR) is employed to construct a non-linear mapping function between the statistical textural histogram features and the perceptual quality score of the distorted LFI. Experimental results on three representative light field databases show that the proposed metric achieves state-of-the-art quality evaluation performance, while having much lower complexity than the existing No-Reference (NR) LF-IQA metrics. The code of the proposed SATV-BLiF metric is available at https://***/ZhengyuZhang96/SATV-BLiF.

关键词： Light field image quality assessment blind local binary pattern textural variation

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