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Character Classification and Actor recognition in Yakshagana Images Using Machine Learning Techniques and Facial Makeup pattern Analysis

Character Classification and Actor Recognition in Yakshagana...

引用

IEEE Distributed computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Anantha Murthy Prathwini Sanjeev Kulkarni Savitha G Department of Master of Computer Applications NMAM Institute of Technology NITTE (Deemed to be University) Nitte Karkala and and Research Scholar Institute of Computer Science and Information Science” Srinivas University India Department of Master of Computer Applications NMAM Institute of Technology NITTE (Deemed to be University) Nitte Karkala India Department of Computer Science and Engineering Srinivas University Institute of Engineering and Technology Mangalore India Department of Data Science and Computer Applications Manipal Academy of higher Education Manipal Institute of Technology Manipal

ISBN: (数字)9798350350593

ISBN: (纸本)9798350350609

Yakshagana, a traditional theater form from Karnataka, India, features a unique combination of vibrant costumes, dynamic dance movements, and elaborate facial makeup, making character and actor identification a challenging task for automated systems. This research work presents a novel approach to classify Yakshagana characters into two primary categories, Vaishnava and Shaiva, and to identify the actors performing these characters using advanced machine learning techniques. Our research employs a Cyclic Gated Recurrent Neural Network (Cyclic GRNN) for classification and identification tasks. For character category classification, we integrate YOLOv3 and Faster R-CNN models, achieving an accuracy of 92.85% with YOLOv3 and 88.285% with Faster R-CNN. The categorization is specifically focused on distinguishing between Vaishnava and Shaiva characters. Additionally, for actor name identification, we utilize a RESNET-50 model, attaining a high accuracy of 95.60%. The results demonstrate the efficacy of Cyclic GRNN combined with state-of-the-art object detection and image classification models in accurately recognizing and categorizing Yakshagana characters and actors. This research contributes to the preservation and digital documentation of Yakshagana by providing robust tools for automated identification, thereby facilitating cultural heritage studies and enhancing audience engagement with this traditional art form.

关键词： YOLO Accuracy Image recognition Recurrent neural networks Machine learning Documentation Logic gates Character recognition Cultural differences Image classification

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Facial expression recognition using improved local binary pattern and min-max similarity with nearest neighbor algorithm 3rd

Facial expression recognition using improved local binary pa...

引用

3rd international conference on Smart Innovations in Communications and Computational Sciences, ICSICCS 2020

作者： Mohan, Narendra Varshney, Neeraj Department of Computer Engineering and Applications GLA University Mathura281406 India

ISBN: (纸本)9789811553448

In human-to-human interaction, important role is played by facial expression recognition (FER). Various modes of understanding and expressions out of verbal world are allowed by this. In many applications, it is a challenging task to analyze human facial expression automatically. For better human machine interaction, robust facial expression recognition (FER) system is proposed in this work. In this work, improved local binary patterns (ILBPs) and min-max similarity with nearest neighbor (MMSNN) algorithm are proposed for efficient training and recognition. Initially, the face detection is performed using Haar classifier method. From images, useful information is extracted by applying improved local binary pattern (ILBP) method. Finally, min-max similarity with nearest neighbor (MMSNN) algorithm is applied to train various expressions of face. Traditional methods are used to make a performance comparison of proposed MMSNN method. Mean recognition rate is achieved by proposed method, which shows its ability in comparison with existing methods. With respect to accuracy, f-measure, recall, and precision, better results are produced by the proposed MMSNN method. © Springer Nature Singapore Pte Ltd 2021.

关键词： Face recognition

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Specialized software Tool for pattern recognition of Biological Objects 11th

Specialized Software Tool for Pattern Recognition of Biologi...

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11th Annual international conference on Brain-Inspired Cognitive Architectures for Artificial Intelligence, BICA*AI 2020

作者： Kulik, Sergey D. Levin, Evgeny O. National Research Nuclear University MEPhI Kashirskoe shosse 31 Moscow115409 Russia Sretenka st. 29 Moscow127051 Russia

ISBN: (纸本)9783030655952

This paper describes the results of work on developing the mobile application for recognition of faces and other biological objects. The application is designed with a focus on loading external machine learning models over the Internet, which allows you to change the model without making any modifications to the application. With such realization, the application can be used in many cases. For example, at carrying out conferences: organizers just need to train a model and send out the link for its downloading to all participants of the conference without any changes in source code. Participants will be able to find out the information about other members they are interested in, as well as contact them directly from the application, both by phone and by e-mail. Instructions are given for teaching your own face recognition model using the Microsoft Custom Vision cloud service, which allows you to train regardless of the power of your local computer. As an example, a classification model was trained and the following assessments of recognition quality indicators were obtained precision: 97.8% and recall: 95.8%. In our future work we consider adding the functionality of emotion recognition based on the pattern recognition algorithm, described in this paper. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Two-Stage Fusion of Local Binary pattern and Discrete Cosine Transform for Infrared and Visible Face recognition 5th

Two-Stage Fusion of Local Binary Pattern and Discrete Cosine...

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5th international conference on Intelligent, Interactive Systems and Applications, IISA 2020

作者： Xie, Zhihua Shi, Ling Li, Yi Key Lab of Optic-Electronic and Communication Jiangxi Science and Technology Normal University Nanchang China

ISBN: (纸本)9783030637835

Combination the corresponding advantage of infrared and visible images, the fusion of visible-light and near-infrared features is becoming one of the key research directions of uncontrolled face recognition. In this paper, a new face fusion recognition method based on near-infrared and visible-light imaging is proposed to make full use of the complementarity in two modalities. Firstly, for near infrared imaging, the low frequency part is retained for discrete cosine transform (DCT) coefficients and the block histogram of local binary pattern (LBP) is jointly applied to represent the discriminative features. Then, LBP features from visible faces are gotten to make up the incapable of local representation in near-infrared imaging. Finally, the three features are sent to the novel two-state fusion module to leverage the performance of face recognition. The experimental results show that the combination of DCT and LBP can extract the complementary features from infrared and visible images, and the two-stage fusion strategy lifts the robustness of the multi-modality face recognition. Especially, the recognition rate of the proposed fusion recognition algorithm has been improved significantly, with regard to the other methods based on statistical feature fusion under the circumstance of small samples. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Local binary pattern

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software Effort Estimation Using Particle Swarm Optimization: Advances and Challenges 2nd

Software Effort Estimation Using Particle Swarm Optimization...

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2nd international conference on Computational Intelligence in pattern recognition (CIPR)

作者： Reddy, Dukka Karun Kumar Behera, H. S. Veer Surendra Sai Univ Technol Dept Informat Technol Burla 768018 India

ISBN: (纸本)9789811524493;9789811524486

The objective of software effort estimation is the analytical calculation for estimating the size, effort, time, and cost of the project, which are interrelated to the software development life cycle. software effort estimation is a composite activity that requires information about various properties that influences the results of the project. The most basic issue is the part of the information required to keep away from unexpected outcomes, but it is difficult to get data in the required quantities for software estimation. At the initial stage of development, the lack of adequacy are certainty, off base, trickiness which are the major constraints of algorithmic and expert judgment estimation models. The non-algorithmic approaches depend on new methodologies like soft computing overcomes the above models. Evolutionary algorithms are theoretical pursuit strategies that copy the natural advancement and the social conduct of the species. Such calculations have been created to land close ideal solutions, for which conventional algorithmic methods may fizzle. The primary point of this study is to give an inside and out survey of particle swarm optimization technique and a general trend toward their use in diverse domains to progress the outcome of the particle swarm optimization algorithm.

关键词： software effort estimation soft computing Particle swarm optimization

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recognition of Character from Handwritten 6

Recognition of Character from Handwritten

引用

6th international conference on Advanced computing and Communication Systems (ICACCS)

作者： Murugan, N. Sivakumar, R. Yukesh, G. Vishnupriyan, J. Chennai Inst Technol Chennai Tamil Nadu India

ISBN: (纸本)9781728151977;9781728151960

This idea deals with the identification of English letters. The input is given to process the character further, it is trained with the computer system modeled on nervous system to identify the design of characters. pattern identification is one of the effective method used in neural networks. The purpose of neural network is to provide algorithm to recognize the letters. The neural network is given with a target which has the design information;it may be a representation of handwritten data with different size and styles. The neural network will establish the exact given details if it matches with the design that neural network has remembered. This project will be useful in identifying the letters of English and Tamil.

关键词： Neural Network pattern recognition

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TEZARNet: TEmporal Zero-Shot Activity recognition Network 30th

TEZARNet: TEmporal Zero-Shot Activity Recognition Network

引用

30th international conference on Neural Information Processing, ICONIP 2023

作者： Deelaka, Pathirage N. De Silva, Devin Y. Wickramanayake, Sandareka Meedeniya, Dulani Rasnayaka, Sanka Department of Computer Science and Engineering University of Moratuwa Moratuwa Sri Lanka School of Computing National University of Singapore Singapore Singapore

ISBN: (纸本)9789819981830

Human Activity recognition (HAR) using Inertial Measurement Unit (IMU) sensor data has practical applications in healthcare and assisted living environments. However, its use in real-world scenarios has been limited due to the lack of comprehensive IMU-based HAR datasets covering various activities. Zero-shot HAR (ZS-HAR) can overcome these data limitations. However, most existing ZS-HAR methods based on IMU data rely on attributes or word embeddings of class labels as auxiliary data to relate the seen and unseen classes. This approach requires expert knowledge and lacks motion-specific information. In contrast, videos depicting various human activities provide valuable information for ZS-HAR based on inertial sensor data, and they are readily available. Our proposed model, TEZARNet: TEmporal Zero-shot Activity recognition Network, uses videos as auxiliary data and employs a Bidirectional Long-Short Term IMU encoder to exploit temporal information, distinguishing it from current work. The proposed model outperforms the state-of-the-art accuracy by 4.7%, 7.8%, 3.7%, and 9.3% for benchmark datasets PAMAP2, DaLiAc, UTD-MHAD, and MHEALTH, respectively. The code is available at https://***/nipdep/TEZARNet © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： pattern recognition

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T-FREX: A Transformer-based Feature Extraction Method from Mobile App Reviews

T-FREX: A Transformer-based Feature Extraction Method from M...

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IEEE international conference on software Analysis, Evolution and Reengineering (SANER)

作者： Quim Motger Alessio Miaschi Felice Dell'Orletta Xavier Franch Jordi Marco Universitat Politecnica de Catalunya Barcelona ItaliaNLP Lab Institute for Computational Linguistics “A. Zampolli” (CNR-ILC) Pisa

ISBN: (数字)9798350330663

ISBN: (纸本)9798350330670

Mobile app reviews are a large-scale data source for software-related knowledge generation activities, including software maintenance, evolution and feedback analysis. Effective extraction of features (i.e., functionalities or characteristics) from these reviews is key to support analysis on the acceptance of these features, identification of relevant new feature requests and prioritization of feature development, among others. Traditional methods focus on syntactic pattern-based approaches, typically context-agnostic, evaluated on a closed set of apps, difficult to replicate and limited to a reduced set and domain of apps. Mean-while, the pervasiveness of Large Language Models (LLMs) based on the Transformer architecture in software engineering tasks lays the groundwork for empirical evaluation of the performance of these models to support feature extraction. In this study, we present T-FREX, a Transformer-based, fully automatic approach for mobile app review feature extraction. First, we collect a set of ground truth features from users in a real crowdsourced software recommendation platform and transfer them automatically into a dataset of app reviews. Then, we use this newly created dataset to fine-tune multiple LLMs on a named entity recognition task under different data configurations. We assess the performance of T- FREX with respect to this ground truth, and we complement our analysis by comparing T- FREX with a baseline method from the field. Finally, we assess the quality of new features predicted by T- FREX through an external human evaluation. Results show that T- FREX outperforms on average the traditional syntactic-based method, especially when discovering new features from a domain for which the model has been fine-tuned.

关键词： software maintenance Reviews soft sensors Named entity recognition Syntactics Feature extraction Transformers

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Neuromorphic Design Using Reward-based STDP Learning on Event-Based Reconfigurable Cluster Architecture 2021

Neuromorphic Design Using Reward-based STDP Learning on Even...

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2021 international conference on Neuromorphic Systems, ICONS 2021

作者： Shahsavari, Mahyar Thomas, David Brown, Andrew Luk, Wayne Imperial College London London United Kingdom University of Southampton Southampton United Kingdom

ISBN: (纸本)9781450386913

Neuromorphic computing systems simulate spiking neural networks that are used for research into how biological neural networks function, as well as for applied engineering such as robotics, pattern recognition, and machine learning. In this paper, we present a neuromorphic system based on an asynchronous event-based hardware platform. We represent three algorithms for implementing spiking networks on our asynchronous hardware platform. We also discuss different trade-offs between synchronisation and messaging costs. A reinforcement learning method known as Reward-modulated STDP is presented as an online learning algorithm in the network. We evaluate the system performance in a single box of our designed architecture using 6000 concurrent hardware threads and demonstrate scaling to networks with up to 2 million neurons and 400 million synapses. The performance of our architecture is also compared to existing neuromorphic platforms, showing a 20 times speed-up over the Brian simulator on an x86 machine, and a 16 times speed-up over a 48-chip SpiNNaker node. © 2021 ACM.

关键词： pattern recognition

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Unsupervised Domain Adaptive Semantic Segmentation Based on Improved DAFormer 22

Unsupervised Domain Adaptive Semantic Segmentation Based on ...

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Proceedings of the 2022 5th international conference on Artificial Intelligence and pattern recognition

作者： Hao Liu Jin-Chun Piao Department of Computer Science and Technology Yanbian University China

ISBN: (纸本)9781450396899

To overcome the intensive of manual labeling tasks at the pixel level required for semantic segmentation under traditional supervised learning, an Unsupervised Domain Adaptive for Semantic Segmentation (UDASS) method based on DAFormer improved model is proposed. This model adapted the Max Mean Discrepancy (MMD) method in the regenerated Hilbert space to help the alignment of the feature distribution, the soft paste strategy to retain the partially covered image blocks to help the model to accelerate convergence, the non-convex consistency regularization at the output level to enhance the robustness of the network, and the spatial pyramid pooling framework and the decoder with large window attention collaboration to improve its consistency. The proposed method was evaluated on the public dataset, and obtained the of 2.4% mIoU improvement in GTA5-to-Cityscapes and 1.1% mIoU in SYSTHIA-to-Cityscapes, respectively, which proved that this method was effective for DAFormer improvement.

关键词： Unsupervised Domain Adaptive Semantic Segmentation (UDASS) consistency regularization soft-paste strategy the max mean discrepancy (MMD)

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