Its a power point presentation on face recognition system . In the covid time biometrics is not a good option thats why we need a face recognition system
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.
This seminar presentation provides an overview of face recognition technology. It discusses how face recognition works by measuring nodal points on the face and creating a numerical face print. The key advantages are that face recognition is convenient, easy to use, and inexpensive compared to other biometrics. However, it cannot distinguish identical twins. The presentation outlines common applications in government (e.g. law enforcement, security), commercial (e.g. banking, access control), and concludes that costs are decreasing which will lead to more widespread deployment of face recognition technologies.
Face recognition technology provides a solution for fast and accurate user identification and authentication by verifying a person's identity based on their face. It works by detecting facial features and measuring the distances between nodal points like the eyes, nose, and jawline to create a unique facial signature or "faceprint". The system then compares new facial images to those stored in a database to match faces or verify identities. While face recognition has advantages like convenience and low cost, it also has limitations such as an inability to distinguish identical twins. It finds applications in security systems, law enforcement, immigration, and banking.
Face recognition is a biometric technique that uses unique facial measurements to identify or verify individuals in images. It analyzes the shape, pattern, and positioning of facial features. Face recognition systems first detect faces in images, then extract distinguishing nodal points like eye depth, nose width, and distance between eyes. They compare these measurements to templates stored in a database to identify matches. While convenient and non-invasive, face recognition has limitations like inability to distinguish identical twins and decreased accuracy with changes in appearance. It finds applications in security, law enforcement, and commercial uses like building access control and ATMs.
Face recognition is a type of biometric software that uses analysis of facial patterns to identify individuals. It has various applications including security, law enforcement, and social media photo tagging. The technology works by measuring nodal points on faces like eye and nose position to create unique numerical faceprints for identification and verification. While effective, face recognition depends on clear images and has limitations with expressions, lighting, or obscured faces. It is increasingly being implemented in areas like access control, immigration, and banking due to lower costs.
This case study examines face recognition technology for e-attendance. It discusses the history of face recognition, defines biometrics, and explains why face recognition was chosen over other biometrics. The key components of face recognition systems are described, including how the systems work by detecting, aligning, normalizing, and matching facial features. Current and potential future applications of the technology are also outlined.
Face detection and recognition using surveillance camera2 editedSantu Chall
The document discusses face detection and recognition technology using surveillance cameras. It describes how face recognition systems work by detecting 80 nodal points on the face and creating a "face print" code based on distance measurements. The document outlines general face recognition steps including face detection, normalization, and identification. It discusses advantages like convenience and passive identification, and disadvantages like inability to distinguish identical twins. Potential applications described include security, law enforcement, immigration, and banking. The document proposes a 12-week project plan to develop a face recognition system prototype.
IRJET-Human Face Detection and Identification using Deep Metric LearningIRJET Journal
This document discusses a project that uses deep metric learning techniques for human face detection and identification in images and videos. Deep metric learning outputs a real-valued vector rather than a single classification. It uses libraries like OpenCV, Dlib, scikit-learn and Keras to build neural networks for facial recognition. The goals are to develop a system that can identify faces even from low quality images with variations in illumination, expression, angle and occlusions. Existing face recognition has challenges in these conditions, so the aim is to improve accuracy rates for normal and non-ideal images through deep metric learning approaches.
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.
This seminar presentation provides an overview of face recognition technology. It discusses how face recognition works by measuring nodal points on the face and creating a numerical face print. The key advantages are that face recognition is convenient, easy to use, and inexpensive compared to other biometrics. However, it cannot distinguish identical twins. The presentation outlines common applications in government (e.g. law enforcement, security), commercial (e.g. banking, access control), and concludes that costs are decreasing which will lead to more widespread deployment of face recognition technologies.
Face recognition technology provides a solution for fast and accurate user identification and authentication by verifying a person's identity based on their face. It works by detecting facial features and measuring the distances between nodal points like the eyes, nose, and jawline to create a unique facial signature or "faceprint". The system then compares new facial images to those stored in a database to match faces or verify identities. While face recognition has advantages like convenience and low cost, it also has limitations such as an inability to distinguish identical twins. It finds applications in security systems, law enforcement, immigration, and banking.
Face recognition is a biometric technique that uses unique facial measurements to identify or verify individuals in images. It analyzes the shape, pattern, and positioning of facial features. Face recognition systems first detect faces in images, then extract distinguishing nodal points like eye depth, nose width, and distance between eyes. They compare these measurements to templates stored in a database to identify matches. While convenient and non-invasive, face recognition has limitations like inability to distinguish identical twins and decreased accuracy with changes in appearance. It finds applications in security, law enforcement, and commercial uses like building access control and ATMs.
Face recognition is a type of biometric software that uses analysis of facial patterns to identify individuals. It has various applications including security, law enforcement, and social media photo tagging. The technology works by measuring nodal points on faces like eye and nose position to create unique numerical faceprints for identification and verification. While effective, face recognition depends on clear images and has limitations with expressions, lighting, or obscured faces. It is increasingly being implemented in areas like access control, immigration, and banking due to lower costs.
This case study examines face recognition technology for e-attendance. It discusses the history of face recognition, defines biometrics, and explains why face recognition was chosen over other biometrics. The key components of face recognition systems are described, including how the systems work by detecting, aligning, normalizing, and matching facial features. Current and potential future applications of the technology are also outlined.
Face detection and recognition using surveillance camera2 editedSantu Chall
The document discusses face detection and recognition technology using surveillance cameras. It describes how face recognition systems work by detecting 80 nodal points on the face and creating a "face print" code based on distance measurements. The document outlines general face recognition steps including face detection, normalization, and identification. It discusses advantages like convenience and passive identification, and disadvantages like inability to distinguish identical twins. Potential applications described include security, law enforcement, immigration, and banking. The document proposes a 12-week project plan to develop a face recognition system prototype.
IRJET-Human Face Detection and Identification using Deep Metric LearningIRJET Journal
This document discusses a project that uses deep metric learning techniques for human face detection and identification in images and videos. Deep metric learning outputs a real-valued vector rather than a single classification. It uses libraries like OpenCV, Dlib, scikit-learn and Keras to build neural networks for facial recognition. The goals are to develop a system that can identify faces even from low quality images with variations in illumination, expression, angle and occlusions. Existing face recognition has challenges in these conditions, so the aim is to improve accuracy rates for normal and non-ideal images through deep metric learning approaches.
This document presents a project on a face recognition system. It provides an abstract describing the use of biometric security systems like face detection and recognition to provide verification and identification capabilities. It then outlines the various sections that will be included in the report, such as introduction, methodology, tools/technologies, applications and future scope. The methodology section describes using an Agile development approach and details the requirements analysis, data modeling, and process modeling steps. Computer vision, image processing and machine learning tools and technologies are also listed.
This document discusses face recognition technology. It begins by defining facial recognition as a type of biometric software that can identify individuals by analyzing patterns in digital images. It then discusses the components and process of how face recognition systems work, including capturing images, extracting nodal point data to create a face print, storing prints in a database, and matching new images to those in the database. The document also covers performance metrics, software, applications, advantages and disadvantages, and concludes that face recognition technology is becoming more cost effective and accurate for various commercial and security uses.
This document discusses face recognition technology. It begins by defining facial recognition as a type of biometric software that can identify individuals by analyzing patterns in digital images. It then discusses the components and process of how face recognition systems work, including capturing images, extracting nodal point data to create a face print, storing prints in a database, and matching new images to those in the database. The document also covers performance metrics, software, applications, advantages and disadvantages, and concludes that face recognition technology is becoming more cost effective and accurate for various commercial and security uses.
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
This document summarizes a seminar report on face recognition technology. It discusses how biometrics can be used to recognize individuals, with physiological biometrics including facial recognition. Face recognition uses computer vision to identify or verify a person's identity from their face. The document then outlines the four stages all identification technologies use - capture, extraction, comparison, and match or non-match. It provides details on how face recognition systems work, the software used, advantages like convenience, and applications in government, commercial, and other sectors.
Face recognition technology uses unique facial features to identify or verify individuals. It works by measuring distances between nodal points on the face, like the eyes, nose, and chin. The technology has various applications and advantages over other biometrics like fingerprints. It does not require physical contact and can identify people quickly without an expert. While very accurate, face recognition may have issues distinguishing between identical twins. The document discusses the components, implementation, advantages and uses of face recognition systems.
This document discusses face recognition technology. It defines biometrics as measurable human characteristics used for identification. Face recognition is a biometric that analyzes facial features from images. It has advantages over other biometrics like fingerprints in not requiring physical contact. The document outlines the process of face recognition including image capture, feature extraction, comparison, and matching. It also discusses factors like accuracy rates and response time.
This document discusses facial recognition technology. It begins with an introduction to biometrics and the need for facial recognition. It then describes the process of facial recognition, including data capture, extraction of features, comparison, and matching. The key components of a facial recognition system and how it works are also outlined. Advantages include convenience and ease of use, while disadvantages relate to issues with lighting, pose, and privacy concerns. The document concludes by describing applications of facial recognition technology in government, security, banking, and other commercial sectors.
seminar presentation on Face ricognition technologyJawhar Ali
This document discusses face recognition technology, which uses computer vision to identify or verify a person's identity based on their face. It describes how face recognition systems work by analyzing nodal points on the face and comparing new images to existing data using techniques like detection, alignment, normalization, and matching. The document also outlines some advantages and disadvantages of this biometric technology, and discusses potential applications in areas like law enforcement, security, banking, and more.
This document provides an overview of biometrics, which involves measuring and analyzing biological data unique to individuals. It discusses various biometric characteristics and technologies, including physiological biometrics like fingerprints, iris recognition, and facial recognition. It also covers behavioral biometrics such as keystroke dynamics, gait recognition, and speaker recognition. For each biometric method, the document discusses industry leaders, use cases, security levels, advantages, and disadvantages. In conclusion, it summarizes uses and applications of biometrics while noting areas of concern regarding different biometrics and their shortcomings.
IRJET- IoT based Door Access Control using Face RecognitionIRJET Journal
This document summarizes an IOT based door access control system using face recognition. The system uses a Raspberry Pi connected to a Pi camera and PIR sensor. When motion is detected, the camera captures an image of the person's face. OpenCV's Haar classifier performs face detection and recognition by comparing the captured image to stored images. If a match is found, the door's servo motor is unlocked remotely through a GSM module. Otherwise, the image is emailed to authorized users who can allow or deny access. The system aims to improve security over traditional key/card based systems by automatically identifying individuals in real-time through facial recognition.
The state of the art and how we applied it at Finnair. This presentation was held in Futurice Munich Beer & Tech event: Brave new world, AI applied. https://www.meetup.com/Munchen-Beer-Tech-Meetup/events/240575870/
Automatic Attendance system using Facial RecognitionNikyaa7
It is a boimetric based App,which is gradually evolving in the universal boimetric solution with a virtually zero effort from the user end when compared with other boimetric options.
This document provides an overview of facial recognition technology and its applications. It discusses how facial recognition systems work by using nodal points on faces to recognize individuals. The objectives are to design software that can accurately detect faces from images without physical interaction and allow for high identification and verification rates. The research methodology involves a workflow for the facial recognition system. Potential applications mentioned include using facial recognition for computer and border security, voting verification, and commercial uses like residential security and banking.
3D Face Recognition Technology in Network Security ApplicationsIRJET Journal
This document discusses 3D face recognition technology and its applications for network security. It begins by describing the limitations of traditional 2D face recognition, such as being affected by changes in lighting, pose, and facial expressions. 3D face recognition uses depth information and is pose invariant. It extracts distinctive facial features like depth of the nose, eye sockets, and chin. The document then covers the working of a 3D face recognition system including detection, alignment, measurement, representation, matching, and verification steps. Finally, it analyzes different algorithms used for face recognition, including Principal Component Analysis (PCA), Discrete Cosine Transform (DCT), and Linear Discriminant Analysis (LDA).
Attendance system based on face recognition using python by Raihan Sikdarraihansikdar
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.
1. The document discusses using facial recognition technology for ATM security to prevent unauthorized access through stolen cards or PINs. It analyzes existing facial recognition methods like eigenfaces and proposes using 3D recognition to address spoofing issues.
2. The methodology section outlines the steps - locating an open source facial recognition program using local feature analysis, extracting features from faces, and searching databases to find matches.
3. Results show that Bank United was the first to use iris recognition at ATMs for a cardless, password-free way to withdraw money. The conclusion is that facial recognition is highly secure and widely used in security applications due to technological advances in identification and verification.
Attendance System using Face RecognitionIRJET Journal
This document describes a proposed attendance system that uses face recognition technology. It begins with an introduction to traditional attendance methods and their limitations. It then discusses the proposed system, which would use face detection and recognition algorithms to automatically mark student attendance from webcam images. Specifically, it would use the Haar cascade algorithm for face detection and KNN (k-nearest neighbors) for face recognition. The document outlines the system design, including an enrollment process to store student face data and an attendance marking process to recognize students in real-time. It suggests this system could automate attendance in a more secure, reliable and time-efficient way compared to traditional methods.
IRJET- Design, Test the Performance Evaluation of Automobile Security Tec...IRJET Journal
This document summarizes a research paper that proposes a face recognition-based security system for automobiles. The system would use algorithms like Eigenfaces and Fisherfaces to identify a person's face and control access to the car door locks based on the recognition. It discusses challenges with face recognition related to variations in lighting, expression, pose etc. The proposed system aims to help reduce car theft attempts by identifying unauthorized persons and sending image/location data to the car owner's phone via an Android app. The document reviews different face recognition techniques and proposes using local binary patterns histograms for their effectiveness in real-time systems.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
This document presents a project on a face recognition system. It provides an abstract describing the use of biometric security systems like face detection and recognition to provide verification and identification capabilities. It then outlines the various sections that will be included in the report, such as introduction, methodology, tools/technologies, applications and future scope. The methodology section describes using an Agile development approach and details the requirements analysis, data modeling, and process modeling steps. Computer vision, image processing and machine learning tools and technologies are also listed.
This document discusses face recognition technology. It begins by defining facial recognition as a type of biometric software that can identify individuals by analyzing patterns in digital images. It then discusses the components and process of how face recognition systems work, including capturing images, extracting nodal point data to create a face print, storing prints in a database, and matching new images to those in the database. The document also covers performance metrics, software, applications, advantages and disadvantages, and concludes that face recognition technology is becoming more cost effective and accurate for various commercial and security uses.
This document discusses face recognition technology. It begins by defining facial recognition as a type of biometric software that can identify individuals by analyzing patterns in digital images. It then discusses the components and process of how face recognition systems work, including capturing images, extracting nodal point data to create a face print, storing prints in a database, and matching new images to those in the database. The document also covers performance metrics, software, applications, advantages and disadvantages, and concludes that face recognition technology is becoming more cost effective and accurate for various commercial and security uses.
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
This document summarizes a seminar report on face recognition technology. It discusses how biometrics can be used to recognize individuals, with physiological biometrics including facial recognition. Face recognition uses computer vision to identify or verify a person's identity from their face. The document then outlines the four stages all identification technologies use - capture, extraction, comparison, and match or non-match. It provides details on how face recognition systems work, the software used, advantages like convenience, and applications in government, commercial, and other sectors.
Face recognition technology uses unique facial features to identify or verify individuals. It works by measuring distances between nodal points on the face, like the eyes, nose, and chin. The technology has various applications and advantages over other biometrics like fingerprints. It does not require physical contact and can identify people quickly without an expert. While very accurate, face recognition may have issues distinguishing between identical twins. The document discusses the components, implementation, advantages and uses of face recognition systems.
This document discusses face recognition technology. It defines biometrics as measurable human characteristics used for identification. Face recognition is a biometric that analyzes facial features from images. It has advantages over other biometrics like fingerprints in not requiring physical contact. The document outlines the process of face recognition including image capture, feature extraction, comparison, and matching. It also discusses factors like accuracy rates and response time.
This document discusses facial recognition technology. It begins with an introduction to biometrics and the need for facial recognition. It then describes the process of facial recognition, including data capture, extraction of features, comparison, and matching. The key components of a facial recognition system and how it works are also outlined. Advantages include convenience and ease of use, while disadvantages relate to issues with lighting, pose, and privacy concerns. The document concludes by describing applications of facial recognition technology in government, security, banking, and other commercial sectors.
seminar presentation on Face ricognition technologyJawhar Ali
This document discusses face recognition technology, which uses computer vision to identify or verify a person's identity based on their face. It describes how face recognition systems work by analyzing nodal points on the face and comparing new images to existing data using techniques like detection, alignment, normalization, and matching. The document also outlines some advantages and disadvantages of this biometric technology, and discusses potential applications in areas like law enforcement, security, banking, and more.
This document provides an overview of biometrics, which involves measuring and analyzing biological data unique to individuals. It discusses various biometric characteristics and technologies, including physiological biometrics like fingerprints, iris recognition, and facial recognition. It also covers behavioral biometrics such as keystroke dynamics, gait recognition, and speaker recognition. For each biometric method, the document discusses industry leaders, use cases, security levels, advantages, and disadvantages. In conclusion, it summarizes uses and applications of biometrics while noting areas of concern regarding different biometrics and their shortcomings.
IRJET- IoT based Door Access Control using Face RecognitionIRJET Journal
This document summarizes an IOT based door access control system using face recognition. The system uses a Raspberry Pi connected to a Pi camera and PIR sensor. When motion is detected, the camera captures an image of the person's face. OpenCV's Haar classifier performs face detection and recognition by comparing the captured image to stored images. If a match is found, the door's servo motor is unlocked remotely through a GSM module. Otherwise, the image is emailed to authorized users who can allow or deny access. The system aims to improve security over traditional key/card based systems by automatically identifying individuals in real-time through facial recognition.
The state of the art and how we applied it at Finnair. This presentation was held in Futurice Munich Beer & Tech event: Brave new world, AI applied. https://www.meetup.com/Munchen-Beer-Tech-Meetup/events/240575870/
Automatic Attendance system using Facial RecognitionNikyaa7
It is a boimetric based App,which is gradually evolving in the universal boimetric solution with a virtually zero effort from the user end when compared with other boimetric options.
This document provides an overview of facial recognition technology and its applications. It discusses how facial recognition systems work by using nodal points on faces to recognize individuals. The objectives are to design software that can accurately detect faces from images without physical interaction and allow for high identification and verification rates. The research methodology involves a workflow for the facial recognition system. Potential applications mentioned include using facial recognition for computer and border security, voting verification, and commercial uses like residential security and banking.
3D Face Recognition Technology in Network Security ApplicationsIRJET Journal
This document discusses 3D face recognition technology and its applications for network security. It begins by describing the limitations of traditional 2D face recognition, such as being affected by changes in lighting, pose, and facial expressions. 3D face recognition uses depth information and is pose invariant. It extracts distinctive facial features like depth of the nose, eye sockets, and chin. The document then covers the working of a 3D face recognition system including detection, alignment, measurement, representation, matching, and verification steps. Finally, it analyzes different algorithms used for face recognition, including Principal Component Analysis (PCA), Discrete Cosine Transform (DCT), and Linear Discriminant Analysis (LDA).
Attendance system based on face recognition using python by Raihan Sikdarraihansikdar
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.
1. The document discusses using facial recognition technology for ATM security to prevent unauthorized access through stolen cards or PINs. It analyzes existing facial recognition methods like eigenfaces and proposes using 3D recognition to address spoofing issues.
2. The methodology section outlines the steps - locating an open source facial recognition program using local feature analysis, extracting features from faces, and searching databases to find matches.
3. Results show that Bank United was the first to use iris recognition at ATMs for a cardless, password-free way to withdraw money. The conclusion is that facial recognition is highly secure and widely used in security applications due to technological advances in identification and verification.
Attendance System using Face RecognitionIRJET Journal
This document describes a proposed attendance system that uses face recognition technology. It begins with an introduction to traditional attendance methods and their limitations. It then discusses the proposed system, which would use face detection and recognition algorithms to automatically mark student attendance from webcam images. Specifically, it would use the Haar cascade algorithm for face detection and KNN (k-nearest neighbors) for face recognition. The document outlines the system design, including an enrollment process to store student face data and an attendance marking process to recognize students in real-time. It suggests this system could automate attendance in a more secure, reliable and time-efficient way compared to traditional methods.
IRJET- Design, Test the Performance Evaluation of Automobile Security Tec...IRJET Journal
This document summarizes a research paper that proposes a face recognition-based security system for automobiles. The system would use algorithms like Eigenfaces and Fisherfaces to identify a person's face and control access to the car door locks based on the recognition. It discusses challenges with face recognition related to variations in lighting, expression, pose etc. The proposed system aims to help reduce car theft attempts by identifying unauthorized persons and sending image/location data to the car owner's phone via an Android app. The document reviews different face recognition techniques and proposes using local binary patterns histograms for their effectiveness in real-time systems.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
1. Submitted in the partial fulfillment for the award of
the degree of
BACHELOR OF ENGINEERING
IN
B.E – CSE (Internet of Things)
DISCOVER . LEARN . EMPOWER
Department of AIT-CSE
Face Recognition System:-
Emotion Detection using Face sentimental analysis in deep face
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Submitted by:
Daksh Pratap Singh
Dhruv Aggarwal
Ankita Bhau
Anuj Jindal
Under the Supervision of:
SUPERVISORS NAME :
Manvinder Singh
2. Outline
• Introduction to Project
• Biometric
• Face recognition
• Future Scope
• Conclusion
• References
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3. Introduction to Project
1. The information age is quickly revolutionizing the way
transactions are completed. Everyday actions are increasingly
being handled electronically, instead of with pencil and paper
or face to face. This growth in electronic transactions has
resulted in a greater demand for fast and accurate user
identification and authentication. Access codes for buildings,
banks accounts and computer systems often use PIN's for
identification and security clearences.
2. Face recognition technology may solve this problem since a
face is undeniably connected to its owner expect in the case
of identical twins. Its nontransferable. The system can then
compare scans to records stored in a central or local
database or even on a smart card.
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4. What are biometrics ?
• A biometric is a unique, measurable characteristic of a human being that can be used to
automatically recognize an individual or verify an individual‟ s identity. Biometrics can
measure both physiological and behavioral characteristics. Physiological biometrics
(based on measurements and data derived from direct measurement of a part of the
human body) include:
• a. Finger-scan
• b. Facial Recognition
• c. Iris-scan
• d. Retina-scan
• e. Hand-scan
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5. Why we choose face recognition over other
biometric ?
• There are number reasons to choose face recognition. This includes the
following :
a. It requires no physical interaction on behalf of the user.
• b. It is accurate and allows for high enrolment and verification rates.
• c. It does not require an expert to interpret the comparison result.
• d. It can use your existing hardware infrastructure, existing camaras and
image capture Devices will work with no problems
• e. It is the only biometric that allow you to perform passive identification
in a one to. Many environments (e.g.: identifying a terrorist in a busy
Airport terminal
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6. FACE RECOGNITION
• The Face – unique part.
• For face recognition there are two types of comparisons
1. Verification.
This is where the system compares the given individual with who that
individual says they are and gives a yes or no decision.
2. Identification.
This is where the system compares the given individual to all the Other
individuals in the database and gives a ranked list of matches
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7. HOW FACE RECOGNITION SYSTEMS WORK
1. If you look at the mirror, you can see that your face has certain distinguishable
landmarks. These are the peaks and valleys that make up the different facial
features. Software defines these landmarks as nodal points.
2. There are about “80 nodal points” on a human face.
• Here are few nodal points that are measured by the software.
• distance between the eyes
• width of the nose
• depth of the eye socket
• cheekbones
• jaw line
• chin
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9. ADVANTAGES AND DISADVANTAGES
• Advantages:
• a. There are many benefits to face recognition systems such as its
convinience and Social acceptability. all you need is your picture taken for it
to work.
• b. Face recognition is easy to use and in many cases it can be performed
without a Person even knowing.
• c. Face recognition is also one of the most inexpensive biometric in the
market and Its price should continue to go down.
• Disadvantages:
• a. Face recognition systems can’t tell the difference between identical
twins.
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10. Conclusion
• Face recognition technologies have been associated generally with very
costly top secure applications. Today the core technologies have evolved
and the cost of equipments is going down dramatically due to the
intergration and the increasing processing power. Certain applications of
face recognition technology are now cost effective, reliable and highly
accurate. As a result there are no technological or financial barriers for
stepping from the pilot project to widespread deployment.
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11. References
1. Electronics for You: - Part 1 April 2001
2. Electronics World: - December 2002
5. www.facereg.com
6. www.Imagestechnology.com
7. www.ieee.com
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