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检索条件"主题词=Local binary pattern"

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Construction of Delaunay Triangles for Face Recognition in Images 14

Construction of Delaunay Triangles for Face Recognition in I...

引用

14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Arunarani, A.R. Samuel, Prithi Sumathy, G. Maheshwari, A. Department of Computational Intelligence SRMIST Kattankulathur Campus Chennai India

A Delaunay graph method is a proposed solution to the matching issue in facial recognition. The techniques for feature matching and feature detection are scale-invariant feature transform (SIFT) and speeded-up robust features (SURF). In the existing method, the major drawback is that each algorithm only extracts particular features, leading to many mismatches in matched features. To avoid those mismatches, this paper concatenates the SIFTSURFLBPCNN together. Input images are pre-processed and key points are detected using sift algorithm. For each Detected key point, textural, and geometrical uutilizing, characteristics are extracted scale-invariant feature transform(SIFT), speeded-up robust features (SURF), local binary pattern(LBP), Convolution neural network(CNN) and concatenated to form a feature vector. Using the lows method, the key points between the input images are filtered out. Then the Delaunay triangles are constructed for the input images from their respective filtered key points using the Delaunay triangulation method. Testing is done and each algorithm’s performance is evaluated. © Grenze Scientific Society, 2023.

关键词： local binary pattern

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Residential Home Surveillance System

Residential Home Surveillance System

引用

2023 IEEE World Conference on Communication and Computing, WCONF 2023

作者： Murugan, Kalpana Reddy, Ramireddygari Dinesh Chandralekha, Neeli Ravikiran, Tiruveedi Deepika, Thota Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Krishnan Koil India

ISBN: (纸本)9798350311204

An unauthorized person, or when someone approaches the home or private space without consent, is referred to as unauthorized access. These issues with unauthorized entry are being dealt with using a variety of techniques. This endeavor shows how the devices that have been built are becoming more capable. This suggested technique incorporates a better strategy meant to improve the exploration operation's ability to work together. In this work, create an application using a computer vision method to stop uninvited visitors from entering the premises by face detection using deep learning with the help of the local binary pattern Histogram (LBPH) algorithm. Anytime a stranger tries to access the property, this application can notify the users. © 2023 IEEE.

关键词： local binary pattern

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Automatic Attendance Monitoring System Using LBPH and HAAR Algorithm 9

Automatic Attendance Monitoring System Using LBPH and HAAR A...

引用

9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Rathinamala, S. Karthikeyan, A. Abishek, R. Kannan, C.K. KIT-Kalaignar Karunanidhi Institute of Technology Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798350397376

Every office and organization must have an attendance monitoring system. Every institution has a standard attendance policy that requires the professor or assistant to report the student's name and attendance for each and every session. An automatic attendance monitoring approach will be suggested to replace this human process. Face spotting, an image processing tool, and face identification techniques are used in the research project that is being suggested. This method assists in turning video frames into numbers so that the human profile may be quickly recognized for attendance monitoring. The image captured from the video should be compared with the data saved with the date and time, which will be automatically stored in the memory storage, before a recognized attendance database is formed. The Viola-Jones Face Detection Technique, also known as the HAAR cascade algorithm, and the local binary pattern Histogram (LBPH) algorithm were used in the development of the proposed automatic face recognition attendance monitoring system. Their combined features are used for attendance monitoring. The human face is first captured using the HAAR cascade technique, and then the LBPH algorithm is utilized to save the distinctive traits of each human face. During the face identification procedure, a predetermined user ID is retrieved from the algorithm data. This will produce the student's attendance data for their presence. The image processing system's data will need to be kept up to date and stored daily. The implementation of an automatic attendance tracking system will benefit from the suggested strategy. Anaconda software is used to implement and analyse the suggested attendance tracking system. © 2023 IEEE.

关键词： local binary pattern

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Video Motion Magnification Based Approach for Improved Recognition of Spontaneous Facial Micro Expressions 3

Video Motion Magnification Based Approach for Improved Recog...

引用

3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Mohite, Prajyot H. Shingare, Pratibha P. Department of Electronics and Telecommunication Maharashtra Pune India

ISBN: (纸本)9798350302288

Facial expression recognition is studied extensively over decades. Recognition of subtle emotions exhibited as micro-expressions is the most challenging task. Being spontaneous, micro-expressions usually exhibited only temporally and entail minute spatial changes and last over very short duration of time, this makes micro expression recognition (MER) significantly challenging compared to non-spontaneous expression recognition. Since the micro-expressions exhibition is at very low intensity level, in order to enhance distinguish-ability, Eulerian Video Magnification (EVM) method is opted for accentuating subtle facial expression. local binary patterns with Six Intersection Points (LBP-SIP) method, is used as a feature extraction technique for dynamic texture analysis in spatial-temporal domain. MER classification process is carried out using Support Vector Machines (SVM) with Radial Basis Function (RBF) kernel. For this proposed method 76.53% MER accuracy is achieved. The process shows enhancement in overall computational efficiency and robustness in high dimensionality (concatenated feature), which is advantageous for practical purpose. © 2023 IEEE.

关键词： local binary pattern

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Utilising Deep Learning as a Law Enforcement Ally 3

Utilising Deep Learning as a Law Enforcement Ally

引用

3rd International Conference on Smart Data Intelligence, ICSMDI 2023

作者： Ramani, D. Roja Nirmala, M. Sourabh, V.N. Chaudhary, Shreyas Kumar, Deepesh New Horizon College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665464871

Finding fugitive offenders after they have committed a crime or an illegal act takes time and effort. It is challenging for law enforcement authorities to complete this work on their own given the rising population density and the size of any nation's landmass. Thus, public participation becomes crucial, revolutionary, and beneficial. This cycle is both times and works seriously. In this paper, we tried to suggest a different framework for criminal Distinguishing & Recognition using Deep learning and Heroku Cloud, i.e. Cloud Computing, which, assuming it is used by our Crime Control Organizations, would help them catch criminals from CCTV images or images uploaded by the public if seen anywhere. This system is in place to assist in capturing criminals and anyone who can upload information indicating that they saw the relevant individual at a specific location and time. In India, where conditions are always changing due to things like light, weather, and specific directions, existing solutions use conventional face acknowledgement computations, which might be problematic because there is no open public contribution. Our research paper employs LBPH, Deep Learning, and Heroku Cloud technologies to construct the system. © 2023 IEEE.

关键词： local binary pattern

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Coalescing color texture and shape texture for an efficient Image Retrieval 4

Coalescing color texture and shape texture for an efficient ...

引用

4th IEEE International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Chelladurai, Callins Christiyana Muthumariappan, Sangeetha Chidambaram, Hari Nainyar Pillai Srm Madurai College For Engineering And Technology Computer Science And Engineering Tamilnadu Sivagangai District India

ISBN: (纸本)9798350384628

It is crucial to create indexing systems, in order to properly access the images, due to the volume of images in the digital world. Content Based Image Retrieval (CBIR) facilitates the indexing to retrieve similar images. This article suggests a CBIR that combines basic attributes from the image for instance shape, color, and texture to retrieve the alike images. The color texture attribute is extracted from the image based on thresholding over color plane and local binary pattern. The shape texture feature is represented by legendre moments from local binary pattern images obtained through wavelet planes. The combination of color texture and shape texture information makes the efficient image representation, which in turn effectively retrieves the similar images from the large set. On the Corel-1K dataset, the suggested approach has been evaluated. Recall and precision measures have been applied to assess the proposed method's effectiveness. According to the experimental findings, the coalesced method based image retrieval system gives notable improvements as compared to systems use either one of color texture or shape texture features. © 2023 IEEE.

关键词： local binary pattern

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Brain Tumour Ordering Using Visual Geometry Group Network

Brain Tumour Ordering Using Visual Geometry Group Network

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Balabantaray, Subhalaxmi Rath, Mamata Samal, Tusharakanta Mishra, Manas Ranjan Engineering College Cuttack India C.V.Raman Global University Odisha India

ISBN: (纸本)9798350300857

A person's life may be significantly impacted negatively by brain tumours as they can impair cognitive function, causing memory loss, trouble concentrating, and personality or behaviour changes. By examining medical imaging data such as magnetic resonance imaging (MRI) scans, classification algorithms can be trained to learn and identify outlines and structures in the pictures that are associated with the existence or nonappearance of a brain tumor and to diagnose it. The general classification challenges in brain tumor classification include tumor heterogeneity, imaging technique limitations, limited sample size, lack of standardization, and incomplete understanding of the underlying biology. In the past, researchers have used various techniques such as traditional machine learning algorithms, deep learning models, and ensemble methods to classify brain tumors. These limitations can often lead to erroneous accuracy at times even after thorough training on a model which may hamper the desired output required for a medical diagnosis. This paper focuses on applying preprocessing methods to the MRI scans of brain tumor in order to establish uniformity among the inputs. Our main objective is to apply a texture descriptor classification which. Thus, our novel approach is focused on an improved algorithm which will prove to be robust to monotonic gray-scale changes caused. The proposed strategy intends to improve the inputs, which will be integrated into a classification algorithm with the efforts to obtain precise results and improved accuracy. The superiority and diversity of the preparation data, as well as the particular parameters and hyperparameters used to train the model, are all factors that affect how well these models function. © 2023 IEEE.

关键词： local binary pattern

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Encrypted Image Retrieval Scheme Based on Neural Network and LBP Mapping 29

Encrypted Image Retrieval Scheme Based on Neural Network and...

引用

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Wang, Lan Wang, Tian Wang, Zheng Jiangsu College of Finance and Accounting School of Ie Lianyungang222061 China

ISBN: (纸本)9798350330717

The privacy-preserving image retrieval research has been a hot issue for cyber-physical system security. Image encryption could effectively meet the growing privacy needs of users. However, the existing searchable encryption schemes are computationally complex, time-consuming, and have low retrieval accuracy. What's more, the image feature extraction requires multiple round communications between the cloud server and the image owner, which causes additional communication burdens. To solve these problems, a privacy-preserving image retrieval scheme combining deep learning technology and local binary pattern (LBP) features is proposed. To protect images, block permutation, order-preserving encryption, and intra-block pixel substitution with multiple tables are used. The encrypted images would be uploaded to the cloud server and the LBP maps of encrypted images were calculated on the cloud side. Then, the neural network was trained by the encrypted LBP maps, and the image features were extracted from them with the trained neural network. The extracted features establish the image index. The Manhattan distance of features was utilized to measure the similarity between the stored images. Finally, the images similar to the query image were returned to the image owner. The security analysis and experimental results show that the proposed scheme is better than most existing schemes regarding security and retrieval accuracy. © 2023 IEEE.

关键词： Computation outsourcing cyber-physical system local binary pattern searchable encryption secure feature extraction

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Drug Consumption Detection by Eye Using Image Processing Techniques

Drug Consumption Detection by Eye Using Image Processing Tec...

引用

2023 International Conference on Computer Communication and Informatics, ICCCI 2023

作者： Pyingkodi, M. Thenmozhi, K. Wilfred Blessing, N.R. Archana, K. Arun Hiruthik, X. Dinesh Kumar, V. Kongu Engineering College Department of Computer Applications Erode India Kristu Jayanti College Department of Computer Science Bengaluru India University of Technology and Applied Sciences-Ibri Department of IT Oman Kongu Engineering College Computer Applications Erode India

ISBN: (纸本)9798350348217

Drug usage is a significant global economic issue that results in countless losses. This study suggests a convolutional neural network-based image processing method for identifying drugged eyes. The front-end packages already in use for this article collect information from eye images using the CNN algorithm. However, it can take some time. As a result, the suggested approach may be used to swiftly and automatically identify drugged eyes. The acquisition of the input image, picture pre-processing, and image processing are the main processes in the suggested *** the reddish regions, emphasising the afflicted regions, confirming the training set, and presenting the findings. Some eyes may go unnoticed, such those that have been socially poisoned. This technique was tried on drugged eyes at different phases, both with and without drug consumption. The technique was used to find a white region of the eye in a given input picture. Images of both drugged and unaffected eyes were presented for training purposes. In order to choose the optimum colour model for this strategy, photos were first transformed to colour models. The Support Erosion technique and the local binary pattern were both used in the model construction for feature extraction. This technique can detect drugged eyes with an average accuracy of 95%. © 2023 IEEE.

关键词： local binary pattern

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local Features Based Spectral Clustering for Defect Detection 1

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2nd International Conference on Pan-African Intelligence and Smart Systems, PAAISS 2022

作者： Tekam Fongouo, Gael Dimitri Mpinda, Berthine Nyunga Tapamo, Jules-Raymond University of KwaZulu-Natal Durban4041 South Africa AIMS-Cameroon Crystal Garden Limbe Cameroon

ISBN: (数字)9783031252716

ISBN: (纸本)9783031252709

Metallurgical industries want to be able to effectively control the quality of their products in order to maintain their prestige and increase their customer base. Therefore, the design and implementation of an optimal technical scheme to detect metal defects would be a solution to this problem. In this work, two methods to extract local characteristics from images of metal products have been presented. These methods are the local binary pattern (LBP) and the Grey Level Co-occurrence Matrix (GLCM). After extracting the specific characteristics describing each image, the information is grouped into a matrix on which the spectral clustering technique is applied in order to classify images according to their similarities. Experiments were carried out and results have shown that defectuous metal surface classification is more accurate when spectral clustering is used in combination with LBP than with GLCM. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： local binary pattern

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