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Face detection ppt
This document presents information on face detection techniques. It discusses image segmentation as a preprocessing step for face detection. Some common segmentation methods are thresholding, edge-based segmentation, and region-based segmentation. Face detection can be classified as implicit/pattern-based or explicit/knowledge-based. Implicit methods use techniques like templates, PCA, LDA, and neural networks, while explicit methods exploit cues like color, motion, and facial features. One method discussed is human skin color-based face detection, which filters for skin-colored regions and finds facial parts within those regions. Advantages include speed and independence from training data, while disadvantages include sensitivity to lighting and accessories.
face detection
This document presents a literature review and proposed work plan for face recognition using a back propagation neural network. It summarizes the Viola-Jones face detection algorithm which uses Haar features and an integral image for real-time detection. The algorithm has high detection rates with low false positives. Future work will apply back propagation neural networks to extract features and recognize faces from a database of facial images in order to build a facial recognition system.
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Face recognition technology uses machine learning algorithms to identify or verify a person's identity from digital images or video frames. The process involves detecting faces, applying preprocessing techniques like filtering and scaling, training classifiers using labeled face images, and then classifying new faces. Common machine learning algorithms used include K-nearest neighbors, naive Bayes, decision trees, and locally weighted learning. The proposed system detects faces, builds a tabular dataset from pixel values, trains classifiers, and evaluates performance on a test set. Software applies techniques like detection, alignment, normalization, and matching to encode faces for comparison. Face recognition has advantages like convenience and low cost, and applications in security, banking, and more.
Face recognition ppt
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Face recognition technology - BEST PPT
Face recognition technology may help solve problems with identity verification by analyzing facial features instead of passwords or pins. The document outlines the key stages of face recognition systems including data acquisition, input processing, and image classification. It also discusses advantages like convenience and ease of use, as well as limitations such as an inability to distinguish identical twins. Potential applications are identified in government, security, and commercial sectors.
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Face detection ppt
This document presents information on face detection techniques. It discusses image segmentation as a preprocessing step for face detection. Some common segmentation methods are thresholding, edge-based segmentation, and region-based segmentation. Face detection can be classified as implicit/pattern-based or explicit/knowledge-based. Implicit methods use techniques like templates, PCA, LDA, and neural networks, while explicit methods exploit cues like color, motion, and facial features. One method discussed is human skin color-based face detection, which filters for skin-colored regions and finds facial parts within those regions. Advantages include speed and independence from training data, while disadvantages include sensitivity to lighting and accessories.
face detection
This document presents a literature review and proposed work plan for face recognition using a back propagation neural network. It summarizes the Viola-Jones face detection algorithm which uses Haar features and an integral image for real-time detection. The algorithm has high detection rates with low false positives. Future work will apply back propagation neural networks to extract features and recognize faces from a database of facial images in order to build a facial recognition system.
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Face recognition technology uses machine learning algorithms to identify or verify a person's identity from digital images or video frames. The process involves detecting faces, applying preprocessing techniques like filtering and scaling, training classifiers using labeled face images, and then classifying new faces. Common machine learning algorithms used include K-nearest neighbors, naive Bayes, decision trees, and locally weighted learning. The proposed system detects faces, builds a tabular dataset from pixel values, trains classifiers, and evaluates performance on a test set. Software applies techniques like detection, alignment, normalization, and matching to encode faces for comparison. Face recognition has advantages like convenience and low cost, and applications in security, banking, and more.
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This document provides an overview of facial recognition technology. It discusses the history of facial recognition, how the technology works by detecting nodal points on faces and creating faceprints for identification. It also covers implementations, comparing images to templates to verify or identify individuals, and applications in security and surveillance. Strengths are its non-invasive nature, but it can be impacted by changes in appearance.
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Face recognition technology may help solve problems with identity verification by analyzing facial features instead of passwords or pins. The document outlines the key stages of face recognition systems including data acquisition, input processing, and image classification. It also discusses advantages like convenience and ease of use, as well as limitations such as an inability to distinguish identical twins. Potential applications are identified in government, security, and commercial sectors.
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This document discusses face detection techniques. It defines face detection as identifying regions in images that contain faces. Face detection is important for applications like security, video retrieval, and human-computer interfaces. The document categorizes face detection methods as either image-based, which use training to compare faces to non-faces, or knowledge-based, which detect facial features like skin, eyes and mouths. It provides examples of techniques within each category and notes that image-based methods are more complex while knowledge-based techniques are usually faster. The document concludes by outlining some open issues in face detection.
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FACE RECOGNITION TECHNOLOGY
INTRODUCTION
FACE RECOGNITION
CAPTURING OF IMAGE BY STANDARD VIDEO CAMERAS
COMPONENTS OF FACE RECOGNITION SYSTEMS
IMPLEMENTATION OF FACE RECOGNITION TECHNOLOGY
PERFORMANCE
SOFTWARE
ADVANTAGES AND DISADVANTAGES
APPLICATIONS
CONCLUSION
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face recognition
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The following resources come from the 2009/10 BSc (Hons) in Multimedia Technology (course number 2ELE0075) from the University of Hertfordshire. All the mini projects are designed as level two modules of the undergraduate programmes.
The objectives of this project are to demonstrate abilities to:
• Handle camera setup, calibrate and capture still and video faces
• Pre-process images and extract features
• Perform face recognition by a) using existing methods and b) trying new techniques.
This project requires the students to apply their abilities to handle image capture hardware and software. Since this is an active area of research, students will need to perform literature survey and discuss ( through brainstorm sessions) their performance characteristics. In addition, they will need to design and implement pre-processing and recognition codes leading to face recognition.Face recognition attendance system
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Introduction
Object detection is a computer technology related to computer vision and image processing that
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cars) in digital images and videos.
Face detection is a computer technology being used in a variety of applications that identifies
human faces in digital images.
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This document discusses face detection and recognition techniques. It introduces the problems of detecting where a face is located in an image (face detection) and identifying who the face belongs to (face recognition). It then describes Viola and Jones' approach which uses AdaBoost learning on Haar-like features computed quickly using integral images to build a classifier cascade that can discard non-face regions and focus on potential face areas. Key steps involve using integral images and Haar-like features for fast computation, AdaBoost for feature selection, and a classifier cascade for efficient scanning.
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This document discusses face recognition systems and the use of artificial neural networks for face recognition. It describes the basic steps in a face recognition system as face detection, alignment, feature extraction, and matching. Two types of neural networks that can be used for recognition are described - Radial Basis Function Networks and Back Propagation Networks. RBF Networks have an input, hidden, and output layer while BPN uses backpropagation of errors to adjust weights. The document also outlines some applications of face recognition systems such as ID verification and criminal investigations.
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Application of image processing
Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.
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1. The document discusses face recognition using an eigenface approach, which uses principal component analysis to extract features from a database of faces to generate eigenfaces that can be used to identify unknown faces.
2. The eigenface approach takes into account the entire face for recognition and is relatively insensitive to small changes in faces. It is faster, simpler, and has better learning capabilities compared to other approaches.
3. Some limitations are that accuracy is affected if lighting and face position vary greatly, it only works with grayscale images, and noisy or partially occluded faces decrease recognition performance.
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This document summarizes a student project on implementing object detection using the Viola-Jones technique. The technique uses Haar feature extraction and an AdaBoost classifier cascade to quickly and accurately detect objects like faces in images. The student developed implementations in Matlab and C++ to train classifiers and detect faces. The Viola-Jones technique was groundbreaking for providing real-time object detection with high accuracy rates compared to previous methods.
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This document is a dissertation submitted by Smriti Tikoo for the fulfillment of requirements for a Master's degree in Electronics and Communication Engineering. The dissertation focuses on facial detection using the Viola-Jones algorithm and facial recognition using a Backpropagation Neural Network. The document begins with an introduction that discusses the history and importance of facial recognition. It then covers topics like facial detection techniques, neural networks, and the proposed methodology which involves Viola-Jones for detection and a Backpropagation Neural Network for recognition. The document is organized into chapters that discuss the literature review, proposed methodology, software implementation, results and discussion, and conclusions.
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This document summarizes a student project to design software that can detect human faces in images. The project's objectives are outlined, including converting images to grayscale and using a Haar cascade classifier to detect faces. Implementation examples like Picasa and Facebook are provided. The procedure involves preprocessing the image, converting it to grayscale, loading face properties, and applying a detection algorithm to find faces. Limitations around orientation are noted, with plans to expand capabilities.
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This document discusses face detection techniques. It defines face detection as identifying regions in images that contain faces. Face detection is important for applications like security, video retrieval, and human-computer interfaces. The document categorizes face detection methods as either image-based, which use training to compare faces to non-faces, or knowledge-based, which detect facial features like skin, eyes and mouths. It provides examples of techniques within each category and notes that image-based methods are more complex while knowledge-based techniques are usually faster. The document concludes by outlining some open issues in face detection.
Attendance system based on face recognition using python by Raihan Sikdar
The document discusses face recognition technology for use in an automatic attendance system. It first defines biometrics and face recognition, explaining that face recognition identifies individuals using facial features. It then covers how face recognition systems work by detecting nodal points on faces to create unique face prints. The document proposes using such a system to take student attendance in online classes during the pandemic, noting advantages like ease of use, increased security, and cost effectiveness. It provides examples of how the system would capture images, analyze features, and recognize enrolled students to record attendance automatically.
FACE RECOGNITION TECHNOLOGY
INTRODUCTION
FACE RECOGNITION
CAPTURING OF IMAGE BY STANDARD VIDEO CAMERAS
COMPONENTS OF FACE RECOGNITION SYSTEMS
IMPLEMENTATION OF FACE RECOGNITION TECHNOLOGY
PERFORMANCE
SOFTWARE
ADVANTAGES AND DISADVANTAGES
APPLICATIONS
CONCLUSION
Automated Face Detection System
The document summarizes an OpenCV based image processing attendance system. It discusses using OpenCV to detect faces in images and recognize faces by comparing features to a database. The key steps are face detection using Viola-Jones detection, face recognition using eigenfaces generated by principal component analysis to project faces into "face space", and measuring similarity by distance between projections.
Attendance Management System using Face Recognition
The project ppt presentation is made for the academic session for the completion of the work from Bharati Vidyapeeth Deemed University(IMED) MCA department
face recognition
face recognition technology.....govt.polytechnic college perumbavvor......ct3 2012,vipin varghese.....
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This presentation tells about how Deep convolutional neural networs is used in facial expression recognition.
Mini Project- Face Recognition
Mini Project- Face RecognitionUniversity of Hertfordshire, School of Electronic Communications and Electrical Engineering
The following resources come from the 2009/10 BSc (Hons) in Multimedia Technology (course number 2ELE0075) from the University of Hertfordshire. All the mini projects are designed as level two modules of the undergraduate programmes.
The objectives of this project are to demonstrate abilities to:
• Handle camera setup, calibrate and capture still and video faces
• Pre-process images and extract features
• Perform face recognition by a) using existing methods and b) trying new techniques.
This project requires the students to apply their abilities to handle image capture hardware and software. Since this is an active area of research, students will need to perform literature survey and discuss ( through brainstorm sessions) their performance characteristics. In addition, they will need to design and implement pre-processing and recognition codes leading to face recognition.Face recognition attendance system
This document summarizes a face recognition attendance system project. The project uses face recognition technology to take attendance by comparing captured images to stored student records. It has a completed status. The methodology follows a waterfall model. System diagrams include context, data flow, and architecture diagrams. The database stores student data like name, roll number, attendance, and captured images. The system allows for student registration by capturing images, training the model, and recognizing faces to mark attendance. Developing this project provided experience with real-world software development processes.
Face Detection and Recognition System
Face Detection and Recognition System (FDRS) is a physical characteristics recognition technology, using the inherent physiological features of humans for ID recognition. The technology does not need to be carried about and will not be lost, so it is convenient and safe for use
An Introduction to Face Detection
Presented by Mr. Dinesh KS
Software Developer, Livares Technologies
Introduction
Object detection is a computer technology related to computer vision and image processing that
deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or
cars) in digital images and videos.
Face detection is a computer technology being used in a variety of applications that identifies
human faces in digital images.
Fake Currency detction Using Image Processing
This document describes a project to develop a system for detecting fake currency using image processing techniques. It discusses how images of currency will be input, preprocessed, segmented, have features extracted and then classified as real or fake using a support vector machine model. The goal is to build a system that can be easily used by the public to verify currency authenticity by analyzing images. It reviews the methodology and components used, including pre-processing, segmentation, feature extraction of texture and other visual properties, and classification. The system is intended to identify fake currency based on differences detected in real currency features.
Face recognigion system ppt
This document provides an overview of facial recognition technology. It discusses the history of facial recognition, how the technology works, its implementation which involves image acquisition, processing, distinctive characteristic location and template matching. It also outlines the strengths and weaknesses of facial recognition as well as its applications in areas like border control, computer security, and banking. While facial recognition provides advantages like convenience and easy use, it also has disadvantages such as being impacted by changes in user appearance.
Face Detection
This document discusses face detection and recognition techniques. It introduces the problems of detecting where a face is located in an image (face detection) and identifying who the face belongs to (face recognition). It then describes Viola and Jones' approach which uses AdaBoost learning on Haar-like features computed quickly using integral images to build a classifier cascade that can discard non-face regions and focus on potential face areas. Key steps involve using integral images and Haar-like features for fast computation, AdaBoost for feature selection, and a classifier cascade for efficient scanning.
Face recognisation system
This document discusses face recognition systems and the use of artificial neural networks for face recognition. It describes the basic steps in a face recognition system as face detection, alignment, feature extraction, and matching. Two types of neural networks that can be used for recognition are described - Radial Basis Function Networks and Back Propagation Networks. RBF Networks have an input, hidden, and output layer while BPN uses backpropagation of errors to adjust weights. The document also outlines some applications of face recognition systems such as ID verification and criminal investigations.
Color Image Processing
This document discusses color image processing and different color models. It begins with an introduction and then covers color fundamentals such as brightness, hue, and saturation. It describes common color models like RGB, CMY, HSI, and YIQ. Pseudo color processing and full color image processing are explained. Color transformations between color models are also discussed. Implementation tips for interpolation methods in color processing are provided. The document concludes with thanks to the head of the computer science department.
Application of image processing
Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.
Drowsiness Detection using machine learning (1).pptx
The document describes a proposed system to detect driver drowsiness using OpenCV and machine learning techniques. The system would use computer vision and facial landmark detection on video from an in-vehicle camera to monitor the driver's eyes and mouth for signs of fatigue like blinking rate, yawning and prolonged eye closures. If drowsiness is detected, the system will alert the driver with an alarm sound and may also activate a self-driving mode if the driver's eyes are closed for over 60 seconds. The proposed system aims to reduce accidents caused by fatigued driving and promote road safety.
Facial recognition system
1. The document discusses face recognition using an eigenface approach, which uses principal component analysis to extract features from a database of faces to generate eigenfaces that can be used to identify unknown faces.
2. The eigenface approach takes into account the entire face for recognition and is relatively insensitive to small changes in faces. It is faster, simpler, and has better learning capabilities compared to other approaches.
3. Some limitations are that accuracy is affected if lighting and face position vary greatly, it only works with grayscale images, and noisy or partially occluded faces decrease recognition performance.
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This document summarizes a 7th semester student project on face detection. It was presented by Nitesh Kumar and Manish Kumar Gupta, under the supervision of Prof. Md. Ehtashamoul Haque. The objectives were to design software to detect human faces in images. It describes what face detection is, the reasons they chose this project, and the basic procedure which involves reading an image, converting it to grayscale, loading facial properties, and applying a detection algorithm to find faces. It provides step-by-step analysis and describes the limitations and future plans to develop an intelligent system that can analyze objects.
DIP
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2. Image enhancement techniques aim to highlight features or bring out obscured details to improve appearance subjectively.
3. Image restoration objectively improves appearance based on models of image degradation.
4. Additional steps include color processing, wavelet transforms, compression, morphological operations, segmentation, representation, object recognition, and use of knowledge bases.
imageprocessing-abstract
The document discusses image processing and describes its goals, applications, and system requirements. It defines image processing as altering existing images in a desired manner to extract important features and provide machine understanding. It provides examples of image processing applications like remote sensing, medical imaging, and character recognition. The proposed system allows users to modify images through tools for compression, rotation, resizing pixels and edge detection, and can process various file formats. Hardware requirements include at least 80GB storage, 512MB RAM, and a Pentium processor, while software requirements include Windows OS, Java/Swing technologies, Apache Tomcat server, and an Oracle or Access backend database.
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This document summarizes a research paper that surveys potential solutions for developing a smart personal AI assistant to help visually impaired people. It discusses using technologies like artificial intelligence, voice recognition, image recognition and text recognition through an Android application. The application could assist users by recognizing surroundings using images, responding to voice commands, and providing text recognition to read text aloud. The paper reviews related works that used technologies like object detection, neural networks and Google's Vision and Dialogflow APIs. It proposes an application with modules for image recognition, speech recognition, interaction with a chatbot, and text recognition to help visually impaired people interact with their environment and carry out daily tasks.
Object detection presentation
The document describes a project that aims to develop a mobile application for real-time object and pose detection. The application will take in a real-time image as input and output bounding boxes identifying the objects in the image along with their class. The methodology involves preprocessing the image, then using the YOLO framework for object classification and localization. The goals are to achieve high accuracy detection that can be used for applications like vehicle counting and human activity recognition.
Face Morphing Be Project
The document describes a student project on face morphing. It includes an abstract, introduction, and literature review sections. The introduction provides an overview of digital image processing and defines the problem of face morphing as developing software to combine parts of different faces into a new composite face. It also discusses expanding/contracting images, blurring edges during morphing, and averaging filter operations. The literature review covers mosaicking images, morphing techniques, and dealing with color images. The overall goal of the project is to develop a program that allows users to edit and combine facial features from a database to generate new composite faces.
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Basics of Digital Image Processing
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2 D3 D Concersion Swaggmedia
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Image Processing in the Current Scenario
This document discusses image processing and its applications. It begins with an abstract discussing image processing and its main types. It then discusses fundamental image processing steps like image acquisition, enhancement, restoration, and more. It also discusses topics like color image processing, wavelets, compression, morphological processing, segmentation, representation and description, and recognition. It provides examples of image processing applications like medical imaging, traffic sensing, image reconstruction, and face detection. It concludes by discussing benefits of image processing and providing examples of image processing techniques like filtering, affine transformations, erosion, dilation and more.
Color based image processing , tracking and automation using matlab
Image processing is a form of signal processing in which the input is an image, such as a photograph or video frame. The output of image processing may be either an image or, a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it. This project aims at processing the real time images captured by a Webcam for motion detection and Color Recognition and system automation using MATLAB programming.
In color based image processing we work with colors instead of object. Color provides powerful information for object recognition. A simple and effective recognition scheme is to represent and match images on the basis of color histograms.
Tracking refers to detection of the path of the color once the color based processing is done the color becomes the object to be tracked this can be very helpful in security purposes.
Automation refers to an automated system is any system that does not require human intervention. In this project I’ve automated the mouse that work with our gesture and do the desired tasks.
Face Recongnition using Machine Learning
This document discusses facial recognition techniques including geometric, eigenfaces, fisherfaces, local binary patterns, active appearance models, 3D shape models, and convolutional neural networks. It outlines the goals of implementing a real-time facial detection and recognition project using a laptop webcam. The methodology that will be used is Dlib's deep learning-based facial recognition which has an accuracy of 99.38% and is easy to implement using its face recognition Python module.
Working With Image
Working with Image basically based on python. It uses python libraries.With the help of this Software we can easily edit our image in just two minutes without using any extra software. It is very easy to use and also user friendly interface made him very effective for editing.
IRJET- 3-D Face Image Identification from Video Streaming using Map Reduc...
This document summarizes a research paper that proposes using 3D face recognition techniques and Hadoop for large-scale face identification from video streams. It describes extracting faces from video frames, representing faces as 3D models with 15 distinguishing features, and using Hadoop for parallel processing to enable fast matching of input faces against a large database of faces. The Hadoop implementation includes map and reduce processes to distribute the face matching computations across multiple servers for improved performance on large datasets.
Digital image processing projects
Takeoff Projects helps students complete their academic projects.You can enrol with friends and receive digital image processing projects kits at your doorstep. You can learn from experts, build latest projects, showcase your project to the world and grab the best jobs. Get started today!
MAJOR PROJECT
This document is a project report on a face recognition and tracking system. It includes an acknowledgements section thanking those who helped with the project. It also includes an abstract describing the project as building a system for face recognition and tracking using image processing and computer vision toolboxes in MATLAB. The document outlines the various chapters that will be included, such as introductions to image processing and the hardware and software used, including Arduino and MATLAB. It provides block diagrams of the overall system design and hardware.
IRJET - Face Recognition based Attendance System
This document describes a face recognition-based attendance system. It begins with an introduction to face recognition and the challenges of implementing such a system in real-time. It then reviews related work on algorithms used for face detection (Haar cascade), feature extraction (Histogram of Oriented Gradients), and recognition (Convolutional Neural Networks). The proposed system is described as collecting a student database, extracting encodings from images using CNN, and comparing real-time detected faces to the database using HOG detection and Euclidean distance matching to mark attendance. Experimental results aimed to test recognition under different training, lighting, and pose conditions.
Lecture 1 computer vision introduction
This document outlines the course details for a Computer Vision course taught by Professor Ahmed Badawi. It includes the course requirements, prerequisites, textbooks, communications methods, intended learning outcomes, course evaluation criteria, and course objectives. The course aims to teach fundamental concepts and algorithms of computer vision to solve real-life problems. Topics covered include image processing techniques, feature detection, segmentation, classification, and recognition. Students will complete assignments applying these techniques and a final project involving face detection or recognition. The course uses a combination of exams, assignments, projects, and attendance for evaluation.
A Novel approach for Graphical User Interface development and real time Objec...
This document presents a novel approach for developing graphical user interfaces and real-time object and face tracking using image processing and computer vision techniques implemented in MATLAB. Key aspects include developing a GUI for image manipulation, object detection and tracking in real-time using color segmentation, expanding this to control a robot, and implementing face detection and tracking using the Viola-Jones algorithm and CAMShift for multiple faces. The approach aims to provide a unified GUI for advanced image processing functions.
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Face detection presentation slide
- 1. CSE 2100 Software Development Project 1 Project Title Face Detection
- 2. Supervisor Mr. Md. Aminul Haque Akhand Assistant Professor Department of Computer Science and Engineering Khulna University of Engineering and Technology Credit: Abu Saleh Md. Musa (0907013) Sanjoy Dutta (0907008)
- 3. Objectives • The objective of our project is to design software that can detect human faces from an image.
- 4. What is Face Detection • Face detection is a computer technology that determines the locations and sizes of human faces in arbitrary (digital) images. It detects facial features and ignores anything else, such as buildings, trees and bodies.
- 5. Why we chose Face Detection Project? • Compatible with Modern Era. • Not common in JAVA. • Basic programme for Recognition(Recognition is not possible without Detection). • Security Maintenance and Media Empowering. • Needed for visual applications in Robotics.
- 6. Examples of Implementation 1 Picasa Photo Viewer (people panel)
- 7. Examples of Implementation 2 • Facebook Tagging
- 8. Procedure at a Glance • Read an image from disk (.JPG, etc.) • Convert it into a jjil.core.Image • Generally we’ll have an RGB image (colored image) and so need to convert it to 8-bit grayscale, which is what the Gray8DetectHaarMultiScale class requires. • Load facial properties to the class form Haar profile for detecting faces. • Apply Gray8DetectHaarMultiScale to our 8-bit grey image. • Retrieve result from Gray8DetectHaarMultiScale.
- 9. Step by Step Analysis Step 1 As part of preprocessing we ensured certain things to make our software functional: • The input is a colored image • There are multiple faces with frontal view and upright orientation • The size of faces within the image should approximately be the same • Little deviation in brightness for all the faces within the image • Faces have to be greater than a certain size in the image so that facial features can be detected. • Standard dimension is not more than 1600 X 1200 px.
- 10. Step 2 • Convert image to jjil.core.Image Where jjil means Jon's Java Imaging Library.
- 11. Step 3 Main Image R/G/B Image 8 bit grayscale
- 12. Step 4 Applying Gray8DetectHaarMultiScale to our 8-bit grey image.
- 13. Final Output
- 14. Limititions • It can’t detect faces without frontal view and upright orientation
- 15. Future Plan • Design an intelligence system that can analyse objects. • Make them enable to see and feel like us. • Remove all its limitations and eager to develop this software. • Enable them to suggest us to make the best use of objects. • Empower media and security services.
- 16. Conclusion • Thanks all