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Advanced Face Detection Using SVM and Haar-like Features: A Comprehensive Approach in AI and Image Processing

Advanced Face Detection Using SVM and Haar-like Features: A ...

引用

Advancement in Computation & Computer Technologies (InCACCT), international conference on

作者： Devendra Somwanshi Archika Jain Kapil Joshi Vishnu Kant Salil Bharany Bura Vijay Kumar Department of EC Poornima College of Engineering Jaipur India Department of CSE Management & Gramothan (SKIT) Swami Keshvanand Institute of Technology Jaipur India Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India School of Computer Science and Artificial Intelligence SR University Warangal

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Face detection is a vibrant research field spanning artificial intelligence, image processing, video surveillance, human-machine interaction, identity authentication, machine learning, pattern recognition, and so on. While humans effortlessly recognize faces, computers face challenges due to facial variability. The complexity lies in factors like expressions, lighting changes, and head positions. Thus, solving the problem of face detection has an important academic value and provides important enlightenment. There are many aspects to face detection such as facial expression, head position, changing lighting conditions and many others. To deal with face detection problems various approaches are used such as template matching methods, knowledge-based methods, feature invariant methods, and appearance-based methods. The proposed approach for face detection utilizes Support Vector machine (SVM) and Haar-like Features. It begins by generating an integral image from a binary image measuring 19 x 19 pixels. Following this, the Haar feature extraction method is employed to obtain Haar values from both face and non-face isommages. A set of 200 face and non-face images from MIT database have been used. Features have been reduced five times to make different features sets of images. These features are passed to Support Vector machine as input parameters and processed to classify face and non-face datasets. Support Vector machine is used to create, train and test the neural network. Features are extracted on MATLAB and further processed in SVM using NeuroSolutions tool. The results are evaluated based on two scenarios as varying number of images and features.

关键词： Support vector machines Training Face recognition Lighting machine learning Feature extraction Magnetic heads Face detection MATLAB Faces

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A Survey of Advancements in DBSCAN Clustering Algorithms for Big Data

A Survey of Advancements in DBSCAN Clustering Algorithms for...

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Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), 2020 international conference on

作者： Omkaresh Kulkarni Adnan Burhanpurwala Deparment of Computer Science and Engineering (AIML) VIIT Kondhwa-BK Pune India Department of Artificial Intelligence and Data Science VIIT Kondhwa-BK Pune India

Cluster analysis is an unsupervised machine learning job of grouping objects based on some similarity measure. Among clustering algorithms, DBSCAN (Density Based Spatial Clustering of Application with Noise) contributes to unsupervised machine learning by enabling the clustering of datasets with varying densities, shapes, and sizes. DBSCAN does not require the predefinition of the number of clusters and is able to recognize noiseless arbitrary clusters by using two parameters, minPts and eps. This paper reviews the different DBSCAN algorithms for big data clustering and provides a detailed comparison among the algorithms.

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Multi-scale fusion clustering algorithm for urban travel features 3

Multi-scale fusion clustering algorithm for urban travel fea...

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2023 3rd international conference on Intelligent Traffic Systems and Smart City, ITSSC 2023

作者： Zhang, Zixiang Zhao, Zhilong Deng, Jiaming Zhao, Yingying Xu, Tao Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Key Laboratory of Geographical Information Science Ministry of Education East China Normal University Shanghai200241 China School of Computer and Information Engineering Henan University Kaifeng475004 China International Education College Henan University Zhengzhou450046 China Henan Technology Innovation Center of Spatio-Temporal Big Data Henan University Zhengzhou450046 China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Zhengzhou450046 China

ISBN: (纸本)9781510672963

Urban travel data typically encompass various temporal and spatial scales and features. Nonetheless, existing traffic travel feature analysis often focuses on a single spatial scale, which may overlook crucial information and hinder features at different scales. Consequently, this could reduce the accuracy of travel pattern recognition. To solve this problem, we introduce a multi-scale fusion transformation algorithm. In our approach, we first establish a multi-level taxi travel data grid system. Then, we apply the I-Clique clustering algorithm to perform grid clustering at each level of the system to identify taxi travel feature regions of different scales. Subsequently, a multi-scale fusion pyramid model is constructed utilizing the acquired travel feature regions at various scales. Finally, we determine the transformation relationships between scales in the pyramid model, enabling the identification of travel feature areas across both known and unknown scales. This approach facilitates the recognition of travel features within cities at any scale. Experimental results demonstrate that the multi-scale fusion transformation algorithm enhances our understanding of spatial aggregation patterns and the hierarchical structure of geographical entities, offering a novel approach for multi-scale urban traffic analysis. © 2024 SPIE. All rights reserved.

关键词： Clustering algorithms

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Face Age Progression with Attribute Manipulation 3rd

Face Age Progression with Attribute Manipulation

引用

3rd international conference on pattern recognition and Artificial intelligence, ICPRAI 2022

作者： Tatikonda, Sinzith Nambiar, Athira Mittal, Anurag Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai India Department of Computational Intelligence SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9783031090363

The human face is one of the predominant means of person recognition. Human faces are affected by many factors i.e. time, attributes, weather, and other subject-specific variations. Although face aging has been studied in the past, the impact of the aforesaid factors, especially, the effect of attributes on the aging process were unexplored. In this paper, we propose a novel holistic "Face Age progression With Attribute Manipulation" (FAWAM) model that generates face images at different ages while simultaneously varying attributes and other subject specific characteristics. We address the task in a bottom-up manner, considering both age and attributes submodules. For face aging, we use an attribute-conscious face aging model with a pyramidal generative adversarial network that can model age-specific facial changes while maintaining intrinsic subject specific characteristics. For facial attribute manipulation, the age processed facial image is manipulated with desired attributes while preserving other details unchanged, leveraging an attribute generative adversarial network architecture. Our proposed model achieves significant qualitative as well as quantitative performance results. © 2022, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Application Research of Computer Artificial intelligence Technology in Image Search and recognition System

Application Research of Computer Artificial Intelligence Tec...

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Electrical Engineering, Big Data and Algorithms (EEBDA), IEEE international conference on

作者： Xin Yu Yuan Li Modern Finance Industry School Shandong Institute of Commerce and Technology Jinan Shandong China

In order to improve the data transfer rate and identification search efficiency of image recognition, a software driver interface design architecture based on FACADE is established in this paper. The PCI device is driven by MDL memory and the vector identification software is designed. Then, a background occlusion method based on microscopic search is proposed to find an efficient decision subset to replace the traditional decision set. This allows efficient retrieval of high-precision image matting. The comparison with the classical matting method proves that the proposed method can effectively overcome the occlusion problem when the foreground and background are highly similar. At the same time, it can effectively reduce the dimension of high-score image matting problem, so as to improve the accuracy of foreground occlusion extraction.

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A Novelty of Face recognition using Hybrid Artificial intelligence Method

A Novelty of Face Recognition using Hybrid Artificial Intell...

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Innovations and Development of Information Technologies and Robotics (IDITR), international conference on

作者： Chalida Pattaranichakul Sunisa Kunarak Department of Electrical Engineering Srinakharinwirot University Nakhon-Nayok Thailand

ISBN: (数字)9798350385694

ISBN: (纸本)9798350385700

In the context of the COVID-19 pandemic that occurred in the past year, people have had to adjust their daily lives to minimize physical contact, including the shift towards online learning, as traditional classroom settings became impractical. Educational institutions have heavily relied on online learning platforms, leading to challenges in measuring and maintaining students' engagement and attendance. Consequently, there has been a surge in research on facial recognition technology to aid in monitoring attendance and participation in online education. However, existing facial recognition systems often face limitations, particularly in terms of excessive data usage for training. The objective of this study is to enhance the efficiency of existing facial recognition systems and reduce resource consumption. In this research, we employed feature extraction techniques in conjunction with GoogLeNet Convolutional Neural Networks and Support Vector machine, specifically focusing on extracting crucial facial features that remain consistent for training. The achieved facial recognition accuracy in this study reached up to 93%.

关键词： Training Support vector machines Technological innovation Accuracy Face recognition Feature extraction Vectors

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Italian-Chinese Neural machine Translation: results and lessons learnt 23

Italian-Chinese Neural Machine Translation: results and less...

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3rd ACM conference on Information Technology for Social Good, GoodIT 2023

作者： Delnevo, Giovanni Im, Marcus Tse, Rita Lam, Chan-Tong Tang, Su-Kit Salomoni, Paola Pau, Giovanni Ghini, Vittorio Mirri, Silvia Department of Computer Science and Engineering University of Bologna Cesena Italy Faculty of Applied Sciences Macao Polytechnic University China Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence Ministry of Education Macao Polytechnic University China

ISBN: (纸本)9798400701160

Today, access to the Internet provides access to various forms of knowledge like free online lecture series offered by prestigious universities, massive open online courses, films and books, and Wikipedia. In addition, it is possible to join online communities on any topic of interest, get to know people with common interests, exchange thoughts and participate in debates. To enable access to these unprecedented knowledge bases, it is crucial to be able to translate texts into any language known by users. For this reason, machine Translation has been a very active research field for the last thirty years. In this paper, we investigate the task of Chinese-Italian translations by exploiting Neural machine Translation approaches. We trained several deep neural networks starting from two already available datasets containing Chinese-Italian parallel corpora. Then, we compared their performance against some of the most common machine translation services freely available online. In particular, we take advantage of Microsoft Translator, Google Translate, DeepL, and ModernMT. © 2023 Owner/Author.

关键词： Neural machine translation

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Comprehensive Visual Features and Pseudo Labeling for Robust Natural Language-based Vehicle Retrieval

Comprehensive Visual Features and Pseudo Labeling for Robust...

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2023 IEEE/CVF conference on Computer Vision and pattern recognition Workshops, CVPRW 2023

作者： Ngo, Bach Hoang Nguyen, Dat Thanh Do-Tran, Nhat-Tuong Huy Thien, Phuc Pham An, Minh-Hung Nguyen, Tuan-Ngoc Hoang, Loi Nguyen Dinh Nguyen, Vinh Dinh, Quang-Vinh Ai Vietnam Research Viet Nam Fpt Telecom Viet Nam Ho Chi Minh City University of Technology Faculty of Computer Science and Engineering Viet Nam Eastern International University School of Computing and Information Technology Viet Nam

ISBN: (纸本)9798350302493

Vehicle retrieval has become crucial for public transportation and intelligent transportation systems due to the exponential development of large-scale transportation videos. Vehicle re-identification and vehicle tracking are the main components of most vision-based vehicle recovery systems. Unfortunately, the limited amount of information provided by traffic video feeds limits the vision-based vehicle retrieval algorithm's efficacy. Therefore, this article proposes a contrastive cross-modal vehicle retrieval approach to maximize the complementarity of natural language and visual representations. An efficient method to fuse multiple input image features is also proposed to extract comprehensive information from various vehicles along with pseudo labeling and efficient post-processing techniques to enhance retrieval accuracy. The proposed method achieved the 3rd ranking of Mean Reciprocal Rank (MRR) score of 0.4795 on the test set for the Challenge Track 2: Tracked-Vehicle Retrieval by Natural Language Descriptions 2023. Source code for the proposed approaches is openly accessible at https://***/anminhhung/AICity-2023-Track2. © 2023 IEEE.

关键词： Image enhancement

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Design of Intelligent Supermarket Service Robot Based on Raspberry Pi

Design of Intelligent Supermarket Service Robot Based on Ras...

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Information Technology,Big Data and Artificial intelligence (ICIBA), international conference on IEEE international conference on

作者： Xue Han Wei Wang Fei Gao School of Intelligence & Electronic Engineering Dalian Neusoft University of Information Dalian China

In this paper, an intelligent supermarket service robot with the functions of speech recognition, intelligent following, voice interaction and automatic payment is realized. It is a 1:20 intelligent robot model, in which Raspberry Pi 4B is the main controller responsible for complex digital image processing and STM32F103RCT6 microcontroller is the auxiliary controller responsible for robot movement, obstacle avoidance and communication. The system uses camera to capture video, and realizes object tracking and commodity QR code recognition through OpenCV image recognition technology. Baidu AI is used for speech recognition and speech synthesis to achieve voice interaction with customers. The robot can also be controlled by APP through Bluetooth communication, and obstacle avoidance can be realized through ultrasonic module. The robot recognizes the QR code of goods, calculates the total price of goods, and generates the payment QR code for customers to realize automatic payment.

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Research on 3D Animation Simulation Based on machine Learning 3

Research on 3D Animation Simulation Based on Machine Learnin...

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3rd Guangdong-Hong Kong-Macao Greater Bay Area Artificial intelligence and Big Data Forum, AIBDF 2023

作者： Huang, Xinran Li, Panpan Tan, Yupeng Zhang, Yining Deng, Mengyuan Chengdu Neusoft University No. 1 Neusoft Avenue Qingchengshan Town Dujiangyan Irrigation Project City Sichuan Province China Fujian University of Technology No. 69 Xuefu South Road Shangjie Town Minhou County Fujian Province Fuzhou City China

ISBN: (纸本)9798400716362

The provided document is a research paper on 3D animation simulation based on machine learning, focusing on the application of machine learning in three-dimensional human animation technology. The paper discusses the importance of virtual reality technology based on computer three-dimensional animation in various fields such as film production, game development, sports simulation, and simulated training. The authors emphasize the significance of three-dimensional human animation technology in portraying subtle changes in human movement and simulating human emotions contained in movement. The paper also explores the application of machine learning methods in processing human body movement data and synthesizing new realistic movement sequences. Various machine learning theories and methods, including regression, function approximation, dimension reduction, classification, clustering, and decision-making, are discussed in relation to their application in three-dimensional human animation technology. The document also reviews the research status of human body movement synthesis technology at home and abroad, highlighting the contributions of various researchers in the field. The authors conclude that machine learning methods have unique advantages in processing human body movement data and can significantly improve the utilization rate of movement capture samples, thereby reducing the cost and time cycle of three-dimensional human animation production. © 2023 Copyright held by the owner/author(s).

关键词： machine learning

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