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5th International Conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Lian, Xiaoqin Liu, Chunquan Gao, Chao Ma, Guochun Gong, Yonggang Li, Jin Beijing Technology and Business University School of Computer and Artificial Intelligence Beijing100048 China China National Light Industry Beijing Technology and Business University Key Laboratory of Industrial Internet and Big Data Beijing100048 China

ISBN: (纸本)9798331506612

In recent years, facial recognition technology has seen rapid advancements and is now extensively utilized in security surveillance and financial transactions. The face recognition process usually includes image preprocessing, feature extraction, and classification. However, it is often difficult for a single feature to fully capture the complex features of a human face, thus affecting the accuracy of recognition. To address this issue, this study presented an AdaBoost-SVM face recognition algorithm that fuses local binary pattern and Gabor features, which can enable comprehensive extraction of facial features from images and enhance recognition accuracy. This method first performs bilateral filtering on the face image to reduce noise and retain edge information. Then, it extracts the local binary pattern and Gabor features and weights the fusion. Finally, it uses the AdaBoost-SVM classifier for identification. Moreover, the algorithm performance was comprehensively evaluated using the ten-fold cross-validation method. The experimental results demonstrated that this method achieves recognition accuracy of 98.75% and 96.25% on ORL and Yale A face databases respectively, and still performs well in complex lighting and expression-changing environments, significantly improving the accuracy of face recognition and its robustness. © 2024 IEEE.

关键词： local binary pattern

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Median arc center corrected binary pattern (MACCBP) for noise robust feature extraction

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MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING 2022年第4期33卷 1409-1444页

作者： Tabatabaei, Sayed Mohamad Chalechale, Abdolah Razi Univ Comp Engn Dept Kermanshah Iran

local binary pattern (LBP) is an efficient texture descriptor with increasing applications in machine vision. Notwithstanding the great ability of LBP in revealing texture features of natural images, this descriptor is sensitive to noise, and its accuracy is reduced when applied to noisy images. The two important noise sensitive components in computing LBP, which affect the generated binary patterns, are the central pixel value and the neighboring pixels values, which are used for thresholding. This paper proposes a noise robust texture descriptor that applies a novel mechanism for potential noisy central pixel detection and correction. Moreover, the proposed descriptor uses a new sampling method that corrects potential noisy neighboring pixels by replacing them with the median of their various radii neighboring pixels, which are located in a new arc-shaped structure. Since the proposed median arc center corrected binary pattern (MACCBP) uses pixels related to different radii patterns for code generation, both macro and micro structures participate in thresholding. Hence, performance is increased in both noisy and noiseless environments. Furthermore, the MACCBP applies the idea of completed LBP and extracts magnitude and center information in conjunction with the sign to achieve more noise robustness and classification accuracy. The proposed descriptor is extensively examined in noisy and noise-free experiments using Outex, UIUC, UMD and CUReT datasets. The experimental results show that MACCBP achieves high classification accuracy in the experiments with original images of the datasets, and when additive salt and pepper noise, Gaussian white noise and Gaussian blur are applied to the test images. The MACCBP is compared with its well-known state-of-the-art counterparts in terms of classification accuracy in the experiments. The results obtained from numerous and extensive tests demonstrate that the proposed descriptor is notably superior to its competitors in noisy

关键词： Texture classification Feature extraction local binary pattern Noise tolerance

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A novel image segmentation utilizing FUZZY-based LBP and active contour model

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IMAGING SCIENCE JOURNAL 2022年第3期70卷 139-154页

作者： Sajadi, Mojtaba Tavakoli, Mohammad Bagher Setoudeh, Farbod Salemi, Amir Hossein Islamic Azad Univ Dept Elect Engn Arak Branch Arak *** Iran Arak Univ Technol Dept Elect Engn Arak *** Iran

This work presents an object recognition algorithm that combines local binary pattern (LBP) based on fuzzy logic approach and active contour model for segmenting different images to detect textured objects. Initially, images containing objects are segmented using the fuzzy logic-optimized LBP method. Then, we eliminate the image noise. Finally, utilizing a Chan-Vese active contour method, the target object of the image is highlighted. The segmentation has been compared with the classical LBP technique and the results indicated higher accuracy and quality for highlighting the object from the background. The classification of highlighted objects is performed with a convolution neural network (CNN). To authenticate the proposed approach, 140 images with the classification of 10 different objects were used. The simulation depicted that the proposed method has better results than other methods both in terms of segmentation error and performance. Significantly, CNN classification also showed a classification accuracy of 92.8%.

关键词： Segmentation active contour model local binary pattern fuzzy logic feature extraction CNN classification segmentation error accuracy

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1 AI-Powered Facial Expression Analysis In Real Time: Unveiling Emotions 1

1 AI-Powered Facial Expression Analysis In Real Time: Unveil...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Aishwarya, D. Sandeep, Ponduru Akhilesh Varma, Penmatsa V.P.S. Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350371802

This study introduces a novel facial deconstruction method for face emotion identification. After identifying facial landmarks with the IntraFace algorithm, seven regions of interest (ROI) are extracted, representing the principal featuresof the face: the mouth, nose, an area between the eyebrows, right and left eyes, and eyebrows. Next, a variety of local descriptors, such as local binary patterns, Completed local binary patterns. local Ternary patterns, and Dynamic local Ternary patterns, are employed to capture distinctive features. Inputting the feature vector which represents a face image into a multi-class support vector machine completes the recognition process. The suggested method outperforms previous facial deconstruction techniques, according on experimental findings on two publicly available datasets. © 2024 IEEE.

关键词： local binary pattern

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Smart Facial Attendance Management System Using Machine Learning 5

Smart Facial Attendance Management System Using Machine Lear...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Joshi, Harnish Bharatia, Nihar Sakariya, Urmil Garg, Dweepna Goel, Parth Department of Computer Engineering Changa388421 India Department of Computer Science and Engineering Changa388421 India

ISBN: (纸本)9798331530013

Face recognition systems are essential in practically every industry in our digital age. One biometric that is frequently utilized is face recognition. It is helpful for security and has a ton of other advantages, identity, and authentication. Despite being less accurate than fingerprint and iris recognition, it is nonetheless commonly utilized because it is a non-invasive and contact-free technique. Additionally, facial recognition systems can be used to track attendance in workplaces, colleges, and schools, among other places. By solving problems with manual attendance procedures, like time consumption and the possibility of proxy attendance, this system seeks to develop an effective facial recognition-based classroom attendance system. As a result, this mechanism becomes more necessary. There are four components to this system capture face, train model, detect and recognize faces, update attendance record, and face detection. The photos of the children in the classroom are what make the data folder. The Haar-Cascade classifier and the local binary pattern Histogram technique are used, respectively, for face detection and recognition. The suggested technique achieves approximately 80% accuracy when three to four students are present. Faces from live streaming video are identified and discovered. As a quick and safe substitute for manual attendance procedures, attendance is tracked in XLS sheets and forwarded to the appropriate faculty members at the conclusion of the session. © 2024 IEEE.

关键词： local binary pattern

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Real-Time Population Tracking & Personal Information Log with Image Processing: Haar Cascade & LBPH Approach

Real-Time Population Tracking & Personal Information Log wit...

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2024 International Conference on Advancements in Power, Communication and Intelligent Systems, APCI 2024

作者： Binish, M.C. Hana, V.K. Fadhiya, N.Z. Anupama, P.T. George, Erin Edward Ernakulam India

ISBN: (纸本)9798350363289

In order to know whether a person is attending a class, meeting, or an assigned task, attendance is taken which is recorded for future clarification. In spite of the traditional method of addressing each person, here we propose the use of cameras with methodologies like Haar cascade and LBPH (local binary patterns Histograms). In this proposal, the user is able to note the attendance of a group of people simultaneously. The faces detected and recognized using these methods are stored in a file. The entire setup can be built using Raspberry Pi, a camera module, and an SD card for storing the data. The setup demonstrates an accuracy of 96 percent during testing. © 2024 IEEE.

关键词： local binary pattern

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Performance Evaluation of AE-RBM Based Pedestrian Detectors in Autonomous Vehicles: Substantial Issues and Challenges 1

Performance Evaluation of AE-RBM Based Pedestrian Detectors ...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Reddy, Abhilash Reddy Pabbath Lal, Madan Boobalan, M. Sapkal, Narayan Pandurang Swetha Reddy, A. Kumar, A.Saran Infobuilders inc Department of IT Georgia University College of Engineering and Technology Department of Mechanical Engineering Bikaner India K S R Institute For Engineering And Technology Department of CSE Tamilnadu Tiruchengode India Dr. D.Y. Patil Institute of Technology Mechanical Engineering Dr. D. Y. Patil Unitech Society's Maharashtra Pune India R.M.K. College of Engineering and Technology Electronics and Communication Engineering Tamilnadu Chennai India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Sathyamangalam India

ISBN: (纸本)9798350371802

Autonomous vehicles' rising profile is a reflection of the widespread interest in them. Improved dependability, reduced fuel consumption, cost savings, and enhanced passenger convenience are all benefits of these cars. However, autonomous vehicles must first have their functionality problems resolved before they can be extensively used on major routes. Pedestrian detection is a crucial component in autonomous vehicles that helps to avoid accidents. Preprocessing, feature extraction, and model training all rely on proper sequencing. A number of filters, such as the Median, Histogram, Dilation and Erosion, Gaussian, mean, and others, can be used to smooth out images before lane marking recognition. Haar wavelet and LBP are the components that make up feature extraction. In order to train for AE-RBM, one must have precise control over the attributes. This method looks so much more modern than the RBM and AE algorithms of today. The results demonstrated a notable enhancement in accuracy, reaching 95.84%. © 2024 IEEE.

关键词： local binary pattern

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Facial Feature Extraction Review for Contemporary applications 15

Facial Feature Extraction Review for Contemporary applicatio...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Singh, Gaganjot Kaur, Shinepreet Devi, Shivali Chandigarh University Department of Computer Science and Engineering Mohali India Department of Computer Science and Engineering India

ISBN: (纸本)9798350370249

Recognizing a face is an intricate cognitive process that showcases the remarkable capabilities of the human brain in visual perception, a phenomenon deeply rooted in evolutionary biology. In an attempt to emulate this complex phenomenon, computer vision technology has been developed to serve as a sophisticated tool capable of recognizing and distinguishing faces, akin to the human visual system, particularly in the domain of biometric identification methods employed for verifying individuals' identities. This is done by identifying the unique features of a human's face through various algorithms. This paper reviews the efficacy of popular approaches to portraying facial features in face pictures. Geometrical-based methods, template-based methods, and appearance-based methods were traditional ways for extracting facial features before the onset of Machine learning for this application. Accuracy through confusion matrix have been comprehended and traditional methods have been found more fit for static and atomic systems although, machine learning algorithms give better and consistent results at the cost of developing and maintaining the computational system underneath. © 2024 IEEE.

关键词： 468 landmark detection discrete wavelet transform eigen faces Facial features detection geodesic distance local binary pattern morphometry

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A Deep Learning Approach to Ringworm and Vitiligo Identification in Skin Imagery 5

A Deep Learning Approach to Ringworm and Vitiligo Identifica...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Kamasani, Kavya Venna, Deepa Badavathula, Harshitha Ranga, Namratha Velagapudi Ramakrishna Siddhartha Engineering College Dept of Computer Science & Engineering Vijaywada India

ISBN: (纸本)9798331504403

Diagnosing skin illnesses such as vitiligo and ringworm can be difficult because of their similarity to other dermatological conditions and their different looks. The suggested approach combines local binary patterns and Grey Level Co-occurrence Matrix, two potent feature extraction techniques. While local binary patterns depicts the local texture patterns inside an image, Grey Level Co-occurrence Matrix analyses the spatial relationships between pixels to collect textural information. These traits are successfully used to depict the feature extraction with the unique qualities of vitiligo and ringworm lesions with accuracy 98%, having been retrieved from dermatological photographs. However, previous machine learning research produced inaccurate results. Then, for classification we tried different deep learning models like Google Net, Mobile Net, ResNet50, ResNet101, Xception, CNN, and VGG19. Among these, we found that VGG19 provided good results for us with an accuracy of about 99.19%. The pre-trained VGG19 model is improved by utilising deep learning and the extracted characteristics to reliably identify and categorise vitiligo and ringworm lesions. The model's discriminative power is increased by integrating Grey Level Co-occurrence Matrix and local binary patterns features, and it maintains strong classification performance with less training data by utilising VGG19. All things considered, the suggested method presents a User Interface for the automated diagnosis of vitiligo and ringworm, giving medical practitioners a useful instrument for early identification and treatment, eventually enhancing patient outcomes and care quality. © 2024 IEEE.

关键词： local binary pattern

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Integrated Evolving Spiking Neural Network and Feature Extraction Methods for Scoliosis Classification

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Computers, Materials & Continua 2022年第12期73卷 5559-5573页

作者： Nurbaity Sabri Haza Nuzly Abdull Hamed Zaidah Ibrahim Kamalnizat Ibrahim Mohd Adham Isa Faculty of Engineering Universiti Teknologi MalaysiaSchool of ComputingSkudai81310JohorMalaysia Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Cawangan Melaka Kampus JasinMerlimau77300MelakaMalaysia Faculty of Computer and Mathematical Sciences University Teknologi MARAShah Alam40450SelangorMalaysia Fakulti Perubatan&Kesihatan Pertahanan Universiti Pertahanan Nasional Malaysia(UPNM)Sungai Besi57000Kuala LumpurMalaysia

Adolescent Idiopathic Scoliosis(AIS)is a deformity of the spine that affects *** current method for detecting AIS is based on radiographic images which may increase the risk of cancer growth due to *** is another alternative used to identify AIS by distinguishing the curves of the spine from the surface of a human’s ***,detecting the curve of the spine is manually performed,making it a time-consuming *** overcome this issue,it is crucial to develop a better model that automatically detects the curve of the spine and classify the types of *** research proposes a new integration of ESNN and Feature Extraction(FE)methods and explores the architecture of ESNN for the AIS classification *** research identifies the optimal Feature Extraction(FE)methods to reduce computational *** ability of ESNN to provide a fast result with a simplicity and performance capability makes this model suitable to be implemented in a clinical setting where a quick result is crucial.A comparison between the conventional classifier(Support Vector Machine(SVM),Multi-layer Perceptron(MLP)and Random Forest(RF))with the proposed AIS model also be performed on a dataset collected by an orthopedic expert from Hospital Universiti Kebangsaan Malaysia(HUKM).This dataset consists of various photogrammetry images of the human back with different types ofMalaysian AIS patients to solve the scoliosis *** process begins by pre-processing the images which includes resizing and converting the captured pictures to gray-scale *** is then followed by feature extraction,normalization,and *** experimental results indicate that the integration of LBP and ESNN achieves higher accuracy compared to the performance of multiple baseline state-of-the-art Machine Learning for AIS *** demonstrates the capability of ESNN in classifying the types of AIS based on photogrammetry images.

关键词： Adolescent idiopathic scoliosis evolving spiking neural network lenke type local binary pattern photogrammetry

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