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Age detection by optimizing the structure of layers and neurons in the neural network

JOURNAL OF OPTICS-INDIA 2024年第2期53卷 1186-1202页

作者： Jiang, Zhenghong Zhou, Chunrong Chongqing Vocat Coll Transportat Sch Big Data Jiangjin 402247 Chongqing Peoples R China

Age detection is a fundamental task in computer vision with numerous applications, from targeted advertising to security systems. This paper proposes a robust approach for age estimation based on local binary patterns to extract features associated with face images. The goal of accurately predicting people's ages from facial images is to overcome challenges such as changes in lighting conditions, poses, and facial expressions. The proposed method uses a combination of feature extraction, feature selection, and machine learning algorithms, which we named Hybrid method. At first, facial landmarks are detected to determine the key points of the face and enable the extraction of the corresponding facial features. These features are then fed into a feature selection algorithm to identify the most distinctive ones, reducing dimensionality and increasing model efficiency. To evaluate the proposed approach, extensive experiments are conducted on benchmark datasets, including different age groups and ethnicities. The results show the effectiveness of the proposed method in achieving high accuracy and robustness in age estimation. As shown in the calculation results, the detection rate and accuracy of Hybrid method age estimation calculations are better than competing methods. For Hybrid method, the mean absolute error is 4.94 years, with a standard deviation of 4.74 years. From the point of view of average absolute error, this age estimation method is superior to other methods that have been presented to date. The proposed method for estimating the age of people has a final sensitivity of 97.2%, an accuracy of 96.8%, and a precision of 99.1%. In addition, it is stated in the specifications of the implementation system that the program can be executed in about 3.5 s, which is a suitable speed for estimating the age of people based on their face photographs.

关键词： ANN Hybrid method Age detection image processing

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Software to Assist Visually Impaired People During the Craps Game Using machine Learning on Python Platform 2nd

Software to Assist Visually Impaired People During the Craps...

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2nd International Conference on Smart Technologies, Systems and applications (SmartTech-IC)

作者： Hernandez Diaz, Nicolas Penaloza, Yersica C. Yuliana Rios, Y. Magre Colorado, Luz A. Univ Tecnol Bolivar Parque Ind & Tecnol Carlos Velez Pombo Cartagena De Indias Colombia Univ Pamplona Km 1 Via Bucaramanga Pamplona Colombia

ISBN: (纸本)9783030991708;9783030991692

Pattern recognition is a prominent area of research in computer vision, where different methods have been proposed in the last 50 years. This work presents the development of a Python API to identify the result of two six-sided dice used in the game called "Craps" as a no-controlled environment to help visually impaired people. The software is structured in four stages. The first one is capturing images through a device with a digital camera connected to the web via IP address. The second stage corresponds to the captured image processing;it is necessary to establish a standard image size and resize and equalize the digitized image. The third stage seeks to segment the object of study by artificial vision techniques to identify the result of the dice after being thrown. Finally, the fourth stage is to interpret the result and play it through a speaker. The expected possible result is a system that integrates the four stages mentioned above through an intuitive and accessible low-cost Python API, mainly aimed at visually impaired people.

关键词： Craps game Visually impaired people Non controlled environment Python API Artificial vision techniques image processing

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3D Object Reconstruction with Deep Learning 13th

3D Object Reconstruction with Deep Learning

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13th IFIP TC 12 International Conference on Intelligent Information processing, IIP 2024

作者： Aremu, Stephen S. Taherkhani, Aboozar Liu, Chang Yang, Shengxiang School of Computer Science and Informatics De Montfort University Leicester United Kingdom Digital Factory Department Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

ISBN: (纸本)9783031579189

Recent advancements and breakthroughs in deep learning have accelerated the rapid development in the field of computer vision. Having recorded a huge success in 2D object perception and detection, a lot of progress has also been made in 3D object reconstruction. Since humans can infer and relate better with 3D world images by just a single view 2D image of the object, it is necessary to train computers to think in 3D to achieve some key applications of computer vision. The use of deep learning in 3D object reconstruction of single-view images is rapidly evolving and recording significant results. In this research, we explore the Facebook well-known hybrid approach called Mesh R-CNN that combines voxel generation and triangular mesh reconstruction to generate 3D mesh structure of an object from a 2D single-view image. Although the reconstruction of objects with varying geometry and topology was achieved by Mesh R-CNN, the mesh quality was affected due to topological errors like self-intersection, causing non-smooth and rough mesh generation. In this research, Mesh R-CNN with Laplacian Smoothing (Mesh R-CNN-LS) was proposed to use the Laplacian smoothing and regularization algorithm to refine the non-smooth and rough mesh. The proposed Mesh R-CNN-LS helps to constrain the triangular deformation and generate a better and smoother 3D mesh. The proposed Mesh R-CNN-LS was compared with the original Mesh R-CNN on the Pix3D dataset and it showed better performance in terms of the loss and average precision score. © IFIP International Federation for Information processing 2024.

关键词： Computer vision

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Movie Recommendation System Based on Emotion Detection Using machine Learning Techniques

Movie Recommendation System Based on Emotion Detection Using...

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Vaishnavi, S.R. Sreelakshmi, S. Anu Prabha, R.S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amritapuri India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382693

The face is a critical perspective in predicting human feelings and moods. More frequently than not human senti-ments are extricated with the utilization of the camera. Various applications are being made based on the location of human sentiments. A few applications of feeling revelation are trade notice suggestion, e-learning, mental clutter, sadness disclosure, criminal conduct discovery, etc. This paper presents a novel real-time emotion-based movie recommendation system that combines computer vision, deep learning, and image processing strategies. The system coordinating OpenCV near DeepFace for effective emotion examination utilizing webcam input, giving clients personalized movie recommendations based on their recognized enthusiastic states. The system commences by using OpenCV to capture real-time webcam feeds and utilizes a Haar-Cascade classifier for facial discovery. The recognized faces are analyzed for prevailing feelings utilizing the DeepFace library, empowering exact feeling distinguishing proof. Within the recommendation stage, the system joins content-based filtering by processing a movie dataset utilizing TF-IDF. Genres and plot keywords serve as features for building the TF-IDF matrix. Cosine similarities between the user's emotion vector and relevant movie genres are then calculated, coming about in a list of personalized movie rec-ommendations. Index Terms-FER, OpenCV, DeepFace, Haar-Cascade Algorithm, Content-Based Filtering, TF-IDF © 2024 IEEE.

关键词： Emotion recognition Visualization Webcams Face recognition Motion pictures Real-time systems Libraries

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2023 5th International Conference on Artificial Intelligence and Computer applications, ICAICA 2023

2023 5th International Conference on Artificial Intelligence...

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2023 5th International Conference on Artificial Intelligence and Computer applications, ICAICA 2023

ISBN: (纸本)9798350323313

The proceedings contain 173 papers. The topics discussed include: restricted area sign detector using YOLO v5;research on distance teaching course interactive system based on computer algorithm research data;APT detection and attack scenario reconstruction based on big data analysis;new image processing: VGG image style transfer with gram matrix style features;trajectory measurement and positioning of underwater vehicle based on monocular stereo vision;the importance of multi feature extraction and fusion for prediction of protein subcellular localization;design and implementation of FPGA-based four-dimensional ultra chaotic system;flocking towards a robust mobile network topology;real time speech recognition method for online complaints from power grid customers based on improved residual network;optimization of parking space detection system based on ZigBee wireless sensor network;and a wire drawing defect detection approach for FDM 3D printing based on machine vision technology.

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Automatic Piped and Micro Irrigation Network 2

Automatic Piped and Micro Irrigation Network

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Ponsudha, P. Shalin, Elton NirmalRaj, D. Rahul, V. Velammal Engineering College Department Of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350365269

This research paper presents cutting-edge technologies and methodologies to enhance precision agriculture and support sustainable farming practices. The study incorporates Satellite image processing for land classification, achieving a remarkable accuracy of 99.7%. The Ultrasonic Pest Repellent system showcases effective pest control with remote capabilities. The C-shaped unit integrates computer vision and machine learning, reducing chemical usage by up to 95%. An IoT-based plant disease detection system achieves superior accuracy in disease classification. The C-shaped Ground Unit addresses challenges faced by Indian farmers, optimizing plant care, nutrient supply, and pest repellence. Together, these innovations contribute to a more sustainable and efficient future for agriculture. © 2024 IEEE.

关键词： Ultrasonic applications

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The Employment of AI and machine Learning methods for Mushroom images Classification using Sequential CNN Model 3

The Employment of AI and Machine Learning methods for Mushro...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Gill, Kanwarpartap Singh Anand, Vatsala Chauhan, Rahul Kukreti, Sanjeev Gupta, Rupesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350319125

The employment of AI and machine learning methods for mushroom classification has several potential uses and applications in the area of AI and related disciplines. It is possible to learn more about biodiversity and ecological health with the use of mushroom taxonomy. The use of AI models for mushroom identification and classification may help us better understand the ecology of a specific area. The work of mushroom picture classification may be both difficult and fascinating. Classifying mushrooms is crucial for a number of reasons, including determining which species are edible and which are dangerous, researching biodiversity, and ecological issues. This study uses a proposed Sequential Convolutional Neural Network (CNN) model for mushroom image classification. Pre-processing of data is done using the graphics processing unit (GPU). The Loss and Accuracy curves are then analysed to provide a visual representation of the Deep Learning model. The proposed model has an expected accuracy of 80%, which would facilitate further study into the categorization of mushrooms. © 2023 IEEE.

关键词： Artificial Intelligence Classification CNN Computer vision Deep Learning image processing Model Training Mushroom image Remote Sensing Sequential Model

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Sub-Pixel counting based diameter measurement algorithm for industrial machine vision

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MEASUREMENT 2024年 225卷

作者： Poyraz, Ahmet Gokhan Kacmaz, Mehmet Gurkan, Hakan Dirik, Ahmet Emir Bursa Tech Univ Dept Elect & Elect Engn TR-16310 Bursa Turkiye Dogu Pres R&D TR-1610 Bursa Turkiye Bursa Uludag Univ Dept Comp Engn TR-16120 Bursa Turkiye

In recent years, there has been a notable surge in the utilization of industrial image processing applications across various sectors, including automotive, medical, and space industries. These applications rely on specialized camera systems and advanced image processing techniques to accurately measure working products with precise tolerances. This research presents a novel fast algorithm for measuring the diameter of a ring, employing a subpixel counting method. The algorithm classifies image pixels into two categories: full pixels and transition pixels. Full pixels reside entirely within the inner region of the workpiece, while transition pixels represent gray pixels that reside at the boundary between the workpiece and its background. To ensure accurate determination of the object area, the proposed method incorporates normalization to account for the contribution of transition pixels alongside full pixels. Subsequently, the circle area equation is employed to calculate the diameter. Moreover, a robust threshold selection method is introduced to effectively distinguish pixels with gray intensities. The experimental setup consists of an industrial camera equipped with telecentric lenses and appropriate illumination. The results demonstrate that the proposed algorithm achieves a 3-10 % improvement in accuracy compared to existing approaches. In terms of measuring sensitivity, the operational sensitivity of the proposed methodology is quantified as 1/20th of the pixel size, exhibiting an average uncertainty of 1 mu m. Furthermore, the proposed method surpasses existing works by at least 12.5 % to 35 % in terms of benchmarking computing time.

关键词： Subpixel Diameter measurement image processing Industrial machine vision Radius O-ring

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Robust Deep Learning Empowered Real Time Object Detection for Unmanned Aerial Vehicles based Surveillance applications

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Journal of Mobile Multimedia 2023年第2期19卷 451-476页

作者： Ranjith, C. Prasanna Hardas, Bhalchandra M. Khaja Mohideen, M. Syed Nijil Raj, N. Robert, Nismon Rio Mohan, Prakash Faculty in Information Technology Department University of Technology and Applied Sciences Shinas Oman Electronics Engineirng Dept Shri Ramdeobaba College of Enginering and Management Nagpur India Department of Information Technology University of Technology and Applied Science Salalah Oman Department of Computer Science and Engineering Younus College of Engineering and Technology Kollam India Department of Computer Science Christ University Bangalore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Surveillance is a major stream of research in the field of Unmanned Aerial Vehicles (UAV), which focuses on the observation of a person, group of people, buildings, infrastructure, etc. With the integration of real time images and video processing approaches such as machine learning, deep learning, and computer vision, the UAV possesses several advantages such as enhanced safety, cheap, rapid response, and effective coverage facility. In this aspect, this study designs robust deep learning based real time object detection (RDL-RTOD) technique for UAV surveillance applications. The proposed RDL-RTOD technique encompasses a two-stage process namely object detection and objects classification. For detecting objects, YOLO-v2 with ResNet-152 technique is used and generates a bounding box for every object. In addition, the classification of detected objects takes place using optimal kernel extreme learning machine (OKELM). In addition, fruit fly optimization (FFO) algorithm is applied for tuning the weight parameter of the KELM model and thereby boosts the classification performance. A series of simulations were carried out on the benchmark dataset and the results are examined under various aspects. The experimental results highlighted the supremacy of the RDL-RTOD technique over the recent approaches in terms of several performance measures. © 2022 River Publishers.

关键词： computer vision deep learning image processing object detection Surveillance unmanned aerial vehicles

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Ferroelectric photosensor network: an advanced hardware solution to real-time machine vision

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NATURE COMMUNICATIONS 2022年第1期13卷 1707页

作者： Cui, Boyuan Fan, Zhen Li, Wenjie Chen, Yihong Dong, Shuai Tan, Zhengwei Cheng, Shengliang Tian, Bobo Tao, Ruiqiang Tian, Guo Chen, Deyang Hou, Zhipeng Qin, Minghui Zeng, Min Lu, Xubing Zhou, Guofu Gao, Xingsen Liu, Jun-Ming South China Normal Univ South China Acad Adv Optoelect Inst Adv Mat Guangzhou 510006 Peoples R China South China Normal Univ South China Acad Adv Optoelect Guangdong Prov Key Lab Opt Informat Mat & Technol Guangzhou 510006 Peoples R China East China Normal Univ Key Lab Polar Mat & Devices Minist Educ Shanghai 200241 Peoples R China South China Normal Univ Natl Ctr Int Res Green Optoelect Guangzhou 510006 Peoples R China Nanjing Univ Lab Solid State Microstruct Nanjing 210093 Peoples R China Nanjing Univ Innovat Ctr Adv Microstruct Nanjing 210093 Peoples R China

Robust, fast, and low-power hardware platforms are desirable for the implementation of real-time machine vision. Here the authors develop a computing-in-sensor network using ferroelectric photo sensors with remanent-polarization-controlled photo responsivities. Nowadays the development of machine vision is oriented toward real-time applications such as autonomous driving. This demands a hardware solution with low latency, high energy efficiency, and good reliability. Here, we demonstrate a robust and self-powered in-sensor computing paradigm with a ferroelectric photosensor network (FE-PS-NET). The FE-PS-NET, constituted by ferroelectric photosensors (FE-PSs) with tunable photoresponsivities, is capable of simultaneously capturing and processing images. In each FE-PS, self-powered photovoltaic responses, modulated by remanent polarization of an epitaxial ferroelectric Pb(Zr0.2Ti0.8)O-3 layer, show not only multiple nonvolatile levels but also sign reversibility, enabling the representation of a signed weight in a single device and hence reducing the hardware overhead for network construction. With multiple FE-PSs wired together, the FE-PS-NET acts on its own as an artificial neural network. In situ multiply-accumulate operation between an input image and a stored photoresponsivity matrix is demonstrated in the FE-PS-NET. Moreover, the FE-PS-NET is faultlessly competent for real-time image processing functionalities, including binary classification between 'X' and 'T' patterns with 100% accuracy and edge detection for an arrow sign with an F-Measure of 1 (under 365 nm ultraviolet light). This study highlights the great potential of ferroelectric photovoltaics as the hardware basis of real-time machine vision.

关键词： Ferroelectrics and multiferroics Information storage

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