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International Conference on Smart Grid and Electrical Automation (ICSGEA)

作者： Zhao, Wenxuan Zhao, Yichao Zhu, Chulei Tianjin Univ Technol & Educ Coll Automat & Elect Engn Tianjin 300350 Peoples R China

ISBN: (纸本)9781728144634

According to the requirements of university classroom teaching, an automatic attendance device is designed, which has the functions of fingerprint recognition and face ***32 is the core of the system,using WIFI module, fingerprint module, GSM module and face recognition technology based on pca algorithm to achieve identity recognition, to meet the needs of attendance in University Classrooms.

关键词： face recognition pca algorithm STM32 attendance check-in

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Face Recognition System using Improved Artificial Bee Colony algorithm

Face Recognition System using Improved Artificial Bee Colony...

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International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)

作者： Khan, Neha Gupta, Manish VITM Dept CSE Gwalior India

ISBN: (纸本)9781467399395

In this paper, Face recognition system using improved artificial bee colony algorithm is proposed. This system works on two different phases, named as Extraction phase and Recognition phase. In the extraction phase, sub-window extraction algorithm is applied on different face images collected from different sources. Then applied artificial bee colony algorithm to reduce number of sub windows, taken from sub window extraction algorithm. This proposed work helps to improve the overhead problem of pca algorithm. Experiments are carried out on different datasets, collected from FACE_94, and VITM datasets. Experimental results show that proposed work efficiency, in terms of recognition rate, is better than original pca algorithm by reducing number of sub-windows.

关键词： Artificial bee colony ABC Sub-window extraction pca algorithm pca Face Recognition

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DPDR: A Novel Machine Learning Method for the Decision Process for Dimensionality Reduction

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SN Computer Science 2024年第1期5卷 1-15页

作者： Dessureault, Jean-Sébastien Massicotte, Daniel LSSI ‑ Laboratory of Signal and System Integration Electrical and Computer Engineering Department Université du Québec à Trois-Rivières 3351 Bd des Forges Trois-Rivières G8Z 4M3 QC Canada

This paper discusses the critical decision process of extracting or selecting the features in a supervised learning context. It is often confusing to find a suitable method to reduce dimensionality. There are pros and cons to deciding between a feature selection and feature extraction according to the data’s nature and the user’s preferences. Indeed, the user may want to emphasize the results toward integrity or interpretability and a specific data resolution. This paper proposes a new method to choose the best dimensionality reduction method in a supervised learning context. It also helps to drop or reconstruct the features until a target resolution is reached. This target resolution can be user defined, or it can be automatically defined by the method. The method applies a regression or a classification, evaluates the results, and gives a diagnosis about the best dimensionality reduction process in this specific supervised learning context. The main algorithms used are the random forest algorithms, the principal component analysis algorithm, and the multilayer perceptron neural network algorithm. Six use cases are presented, and every one is based on some well-known technique to generate synthetic data. This research also discusses each choice that can be made in the process, aiming to clarify the issues about the entire decision process of selecting or extracting the features. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction Feature selection MLP neural network pca algorithm Random forest algorithm

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Analyzing the Impact of Principal Component Analysis on k-Nearest Neighbors and Naive Bayes Classification algorithms 29th

Analyzing the Impact of Principal Component Analysis on k-Ne...

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29th International Conference on Information and Software Technologies (ICIST)

作者： Macionczyk, Rafal Moryc, Michal Buchtyar, Patryk Silesian Tech Univ Fac Appl Math Kaszubska 23 PL-44100 Gliwice Poland

ISBN: (纸本)9783031489808;9783031489815

Principal Component Analysis (pca) is a well-known dimensionality reduction technique that has been widely used in various machine learning algorithms. This includes kNN and Naive Bayes algorithms which can be time-consuming. The reduction of dimensions can have positive effects on those two algorithms by reducing the number of related types of data and decreasing the data they need to analyze. Here we present detailed findings about how the pca algorithm affects them both in time efficiency and accuracy. All calculations regarding those values were carried out in Python programming language. The dataset used in research is the Titanic dataset, on which data cleaning and normalization were done. The data in this paper suggests that it is possible to maintain the same level of accuracy with great improvement in time efficiency. For the kNN algorithm reducing the number of dimensions by one resulted in a 31.09% increase in accuracy and for the Naive Bayes algorithm an 18.18% increase while having an imperceptible effect on accuracy.

关键词： pca algorithm kNN algorithm Naive Bayes Classification algorithms analyze impact effect

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Low-Power Home Embedded Surveillance System Using Image Processing Techniques

Low-Power Home Embedded Surveillance System Using Image Proc...

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International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems (ICAIECES)

作者： Arathi, K. Pillai, Anju S. Amrita Vishwa Vidyapeetham Univ Coimbatore Tamil Nadu India

ISBN: (纸本)9788132226567;9788132226543

The need for surveillance systems are increasing due to safety and security requirements. And there exists an ample amount of challenging work yet to be explored in this domain. The current work proposes design, development and implementation of a low-power home embedded surveillance system. Such systems being operated throughout the day, consumes considerable amount of power. Power consumption being a crucial design parameter affects the utility of the system. In the proposed system to consume power, use of low-power sensor groups and controlling the activation of surveillance camera is incorporated through the M-bed microcontroller. Presence of a person is detected and face recognition is carried out using various image processing techniques.

关键词： Surveillance system PIR sensor Ultrasonic sensor Image processing Viola-Jones detection pca algorithm Gamma correction

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Face Recognition on Surgically Altered Faces using Principal Component Analysis

Face Recognition on Surgically Altered Faces using Principal...

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IEEE International Conference on Circuit ,Power and Computing Technologies (ICCPCT)

作者： George, Giji Boben, Rainu Radhakrishnan, B. Suresh, L. Padma BMCE CSE Dept Sasthamcotta Kerala India

ISBN: (纸本)9781509049677

The popularity of plastic surgery is increasing due to the enhancement and accessibility in that field. Plastic surgery procedure provides an efficient way to improve the facial appearance, which result in the reconstruction of facial features either globally or locally. The changes introduced by plastic surgery remains complicated to be analyzed by face recognition systems. This paper focus on recognition of face images before and after plastic surgery. Here, Viola-Jones algorithm is used to detect the face and modified pca algorithm is used to recognize faces from images with some near real-time variations. This principal component analysis resolves the recognition problem for 2-D image of faces, using Eigenface Recognition algorithm.

关键词： Face Detection Viola-Jones algorithm Face Recognition pca algorithm Eigenface approach Euclidian Distance

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Body shape variability problem in eigenspace

Body shape variability problem in eigenspace

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Conference on Image Processing - algorithms and Systems II

作者： Rahman, AM Ishikawa, S Kyushu Inst Technol Dept Mech & Control Engn Kitakyushu Fukuoka 8048550 Japan

ISBN: (纸本)0819448141

This paper claims that human various body shapes create individual eigenspaces;as a result, the classical appearance-based model cannot be effective for recognizing human postures. We introduce 'figure effect' in the eigenspaces due to different human body shapes in this particular study. The study proposes an organized eigenspace tuning method for overcoming the preceding problem. Since the proposed method tunes the classical eigenspaces for human posture recognition, we define this phenomenon 'eigenspace tuning'. Generation of a tuned eigenspace (TES) is an organized method where some of similar eigenspaces are selected according to MDD (minimum description deviation) method and a mean if them is taken. In fact, the TES is an optimized visual appearance of various human models that minimizes the fluctuation of MDL (mean description length) between training and testing feature spaces. We have tested the proposed approach on a number of human models considering their various body shapes, and significance of the method to the recognition rates has been demonstrated.

关键词： appearance based eigenspace tuned eigenspace dress effect figure effect pca algorithm Gaussian-Laplacian image

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Fast Audio Fingerprint Search Strategy for Song Identification

Fast Audio Fingerprint Search Strategy for Song Identificati...

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1st International Conference on Networking and Digital Society

作者： Shen, Ling Guan, Yaohua Wu, Yun Zhao, Yong Peking Univ Shenzhen Grad Sch Key Lab Integrated Microsyst Shenzhen 518055 Peoples R China

ISBN: (纸本)9780769536354

In this paper we present an audio fingerprinting (AF) system for song identification. For the high dimensional audio fingerprint data, two AF searching algorithms were proposed and implemented: Principle Component Analysis (pca) and the summation of the corresponding data between different frames. The experimental results show that applying pca algorithm, the accuracy is 94.98% while the search time is as low as 8.42%;applying Sum algorithm, the accuracy is 95.92% while the search time is as low as 3.72%. A top-N Approximate Nearest Neighbor (ANN) searching is applied to the dimension-reduced data before the exact search of N full dimension data. The final realization shows an accuracy rate of 96.11% with 8.85% of search time compared with the full search method.

关键词： audio fingerprinting pca algorithm suns algorithm approximate nearest neighbor searching

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MATLAB Implementation of Face Identification using Principal Component Analysis

MATLAB Implementation of Face Identification using Principal...

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International Conference on Materials Science and Information Technology (MSIT 2011)

作者： Mahmood, Nasrul Humaimi Ariffin, Ismail Omar, Camallil Jaafar, Nur Sufiah Univ Teknol Malaysia Biomed Instrumentat & Elect Res Grp Dept Elect Fac Elect Engn Utm Johor Bahru 81310 Malaysia

ISBN: (纸本)9783037853191

Face is the greatest superior biometric as the face has a complex, multidimensional and meaningful identity compared from one person to another. Face identification is executed by comparing the characteristics of the face (test image) with those of known individual images in the database. This paper describes the used of the Principal Component Analysis (pca) algorithm for human face identification based on webcam image. The MATLAB is used as a tool for image processing and analysis. The important decision to identify the person is by the minimum distance of the face images and known face images in face space. From the results, it can be concluded that the work has successfully implemented the pca algorithm for human face identification through a webcam.

关键词： pca algorithm Face Identification Webcam Biometrics MATLAB

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Human Behavior Prediction using Facial Expression Analysis

Human Behavior Prediction using Facial Expression Analysis

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IEEE International Conference on Computing, Communication and Automation (ICCCA)

作者： Shakya, Subarna Sharma, Suman Basnet, Abinash Tribhuvan Univ Inst Engn Dept Elect & Comp Engn Cent Campus Lalitpur Nepal Kathmandu Engn Coll Dept Elect & Comp Engn Kathmandu Nepal

ISBN: (纸本)9781509016662

Computer Vision seeks to emulate human vision by analyzing digital image inputs as human perception does. To detect an emotion will not be a difficult task to for human but for any computer, detecting an emotion will be a difficult job to perform as they are unaware of that human nature. In our paper, first of all, we converted a video into frame sequence which is used for human behavior prediction by correlating frames. To analyze face, a face is detected and the ROI for expression using Viola-Jones AdaBoost method on color filtered image algorithm to detect people's faces, noses, eyes, mouth, or upper body, as these parts have higher entropy for emotion detection. We used a pca algorithm for feature extraction which calculates coefficient, score, and latent;where latent is used to calculate Eigenvector. As we found that left to right and top to the bottom approach to facial movement gives facial gesture change and facial expression change respectively and so on. For human emotion prediction, we used Euclidian distance calculation. From the various sequence of emotion, appropriate behavior is predicted. Finally, we have developed an algorithm for continuous tracking and monitoring the moving suspicious faces continuously using Kalman filter estimation. The proposed method is simple and very useful for the high-security alert zones e.g. hospital, airport, bank etc.

关键词： edge detection Euclidian distance feature extraction ROI pca algorithm

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