ppt ofSMARTBRAIN of gita autonomous college
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E-COMMERCE WEBSITE WITH AR-BASED TECHNOLOGY
1) The document describes the development of an e-commerce website that uses augmented reality (AR) technology. This allows customers to virtually try on products from home using their smartphone cameras and AR features.
2) A mediapipe holistic model is used for body detection and landmark identification. This model identifies more landmarks than openpose, increasing detection accuracy.
3) The website allows customers to select a product, access an AR view using their camera, and see a virtual rendering of the product overlaid on their body using detected landmarks. This helps customers make selection decisions without visiting a physical store.
IRJET - Augmented Reality: Social Profile Detection by Face Recognition
This document discusses using augmented reality and face recognition techniques to display a person's social media profile by analyzing their face. It proposes combining AR and face recognition by developing a device that can recognize a person's social status from their face in a crowd. The document reviews related works using AR for product promotion, education, and shopping applications. It describes existing face recognition and AR display methods, and algorithms like Eigenface and Haar cascade SVM classifier that can be used. The aim is to enhance interaction between users and technology, with applications in criminal detection, disaster management, education and more.
Augmented Reality for Smart Profile Display Part-2
This document describes a proposed augmented reality system to display student profiles and information in educational institutions. The system uses three modules: 1) ID card recognition, where scanning a student's ID card retrieves their information from a database. 2) Voice detection, where saying a student's name retrieves their information. 3) Face recognition, where scanning a student's face retrieves their information. The system was designed and tested using visual studio and an online database to augment student profiles and details onto the screen in real-time when their ID, name, or face is detected. The system aims to easily and quickly retrieve student information through various augmented reality methods.
Mouse Cursor Control Hands Free Using Deep Learning
1) The document presents a study on developing a mouse-free cursor control system using facial movements and deep learning.
2) The system uses facial landmark detection and analysis of facial ratios like eye aspect ratio and mouth aspect ratio to interpret facial expressions and predict cursor movement directions.
3) Support vector machines and histogram of oriented gradients algorithms are used to train the system to classify facial features and link them to cursor control functions like moving, clicking, and scrolling.
Mouse Cursor Control Hands Free Using Deep Learning
1) The document presents a study on developing a mouse-free cursor control system using facial movements and deep learning.
2) The system uses facial landmark detection and analysis of facial ratios like eye aspect ratio and mouth aspect ratio to detect facial expressions and movements like winks, blinks, and mouth movements.
3) A support vector machine (SVM) classifier is trained on the facial features to predict and translate cursor movement directions based on the user's facial gestures, allowing them to control the cursor and perform actions hands-free.
IRJET- Survey on Virtual Assistants
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CAMPUS MANAGEMENT SYSTEM WITH ID CARD USING FACE RECOGNITION WITH LBPH ALGORITHM
The Face recognition system mentioned is a computer vision and image processing application designed to
carry out two primary functions: identifying and verifying a person from an image or a video database.
The objective of this research is to provide a more efficient and effective alternative to traditional manual
management systems. It can be used in offices, schools, and organizations where security is critical. In the
proposed system, initially, all students enrolled in the Academic Year whose information is stored in a
database server and released a unique ID Card with their facial image to be a smart campus. The main
objective of the proposed system is to automate the time-in, and time-out of students, teachers, staff, and
anyone who enters and leaves the campus of the University of Computer Studies, Hinthada (UCSH). This
system is implemented with the 405 students in the 2022-2023 Academic Year, 86 permanent staff including
the principal whose ID card (Name, Year, Roll No, NRC, Father Name: for students, Name, Rank,
Department, NRC, Address: for teachers and staffs). As soon as someone enters the campus of a university,
the ID card is scanned, the images of the ID card are captured and the face onthe card will be matched
with the faces in the trained dataset to detect by using the Haar cascade classifier, and recognize the face
using Local Binary Pattern LBPH algorithm. The proposed system demonstrates strong performance,
achieving an accuracy rate of over 90% for everyone entering the campus. It is both effective and efficient,
providing a smart solution for identification.
Campus Management System with ID Card using Face Recognition with LBPH Algorithm
The Face recognition system mentioned is a computer vision and image processing application designed to carry out two primary functions: identifying and verifying a person from an image or a video database. The objective of this research is to provide a more efficient and effective alternative to traditional manual management systems. It can be used in offices, schools, and organizations where security is critical. In the proposed system, initially, all students enrolled in the Academic Year whose information is stored in a database server and released a unique ID Card with their facial image to be a smart campus. The main objective of the proposed system is to automate the time-in, and time-out of students, teachers, staff, and anyone who enters and leaves the campus of the University of Computer Studies, Hinthada (UCSH). This system is implemented with the 405 students in the 2022-2023 Academic Year, 86 permanent staff including the principal whose ID card (Name, Year, Roll No, NRC, Father Name: for students, Name, Rank, Department, NRC, Address: for teachers and staffs). As soon as someone enters the campus of a university, the ID card is scanned, the images of the ID card are captured and the face onthe card will be matched with the faces in the trained dataset to detect by using the Haar cascade classifier, and recognize the face using Local Binary Pattern LBPH algorithm. The proposed system demonstrates strong performance, achieving an accuracy rate of over 90% for everyone entering the campus. It is both effective and efficient, providing a smart solution for identification.
Recommended
E-COMMERCE WEBSITE WITH AR-BASED TECHNOLOGY
1) The document describes the development of an e-commerce website that uses augmented reality (AR) technology. This allows customers to virtually try on products from home using their smartphone cameras and AR features.
2) A mediapipe holistic model is used for body detection and landmark identification. This model identifies more landmarks than openpose, increasing detection accuracy.
3) The website allows customers to select a product, access an AR view using their camera, and see a virtual rendering of the product overlaid on their body using detected landmarks. This helps customers make selection decisions without visiting a physical store.
IRJET - Augmented Reality: Social Profile Detection by Face Recognition
This document discusses using augmented reality and face recognition techniques to display a person's social media profile by analyzing their face. It proposes combining AR and face recognition by developing a device that can recognize a person's social status from their face in a crowd. The document reviews related works using AR for product promotion, education, and shopping applications. It describes existing face recognition and AR display methods, and algorithms like Eigenface and Haar cascade SVM classifier that can be used. The aim is to enhance interaction between users and technology, with applications in criminal detection, disaster management, education and more.
Augmented Reality for Smart Profile Display Part-2
This document describes a proposed augmented reality system to display student profiles and information in educational institutions. The system uses three modules: 1) ID card recognition, where scanning a student's ID card retrieves their information from a database. 2) Voice detection, where saying a student's name retrieves their information. 3) Face recognition, where scanning a student's face retrieves their information. The system was designed and tested using visual studio and an online database to augment student profiles and details onto the screen in real-time when their ID, name, or face is detected. The system aims to easily and quickly retrieve student information through various augmented reality methods.
Mouse Cursor Control Hands Free Using Deep Learning
1) The document presents a study on developing a mouse-free cursor control system using facial movements and deep learning.
2) The system uses facial landmark detection and analysis of facial ratios like eye aspect ratio and mouth aspect ratio to interpret facial expressions and predict cursor movement directions.
3) Support vector machines and histogram of oriented gradients algorithms are used to train the system to classify facial features and link them to cursor control functions like moving, clicking, and scrolling.
Mouse Cursor Control Hands Free Using Deep Learning
1) The document presents a study on developing a mouse-free cursor control system using facial movements and deep learning.
2) The system uses facial landmark detection and analysis of facial ratios like eye aspect ratio and mouth aspect ratio to detect facial expressions and movements like winks, blinks, and mouth movements.
3) A support vector machine (SVM) classifier is trained on the facial features to predict and translate cursor movement directions based on the user's facial gestures, allowing them to control the cursor and perform actions hands-free.
IRJET- Survey on Virtual Assistants
The document describes a proposed virtual assistant that uses facial recognition and speech recognition to overcome limitations of existing assistants that are dependent on single devices. The proposed assistant would recognize users through facial recognition and interact with them across different platforms. It would have several key services including face detection, speech detection, and a dialogue manager to generate responses from a knowledge database. The authors suggest this type of device-independent virtual assistant could have applications in education, healthcare, vehicles, home automation, and security.
CAMPUS MANAGEMENT SYSTEM WITH ID CARD USING FACE RECOGNITION WITH LBPH ALGORITHM
The Face recognition system mentioned is a computer vision and image processing application designed to
carry out two primary functions: identifying and verifying a person from an image or a video database.
The objective of this research is to provide a more efficient and effective alternative to traditional manual
management systems. It can be used in offices, schools, and organizations where security is critical. In the
proposed system, initially, all students enrolled in the Academic Year whose information is stored in a
database server and released a unique ID Card with their facial image to be a smart campus. The main
objective of the proposed system is to automate the time-in, and time-out of students, teachers, staff, and
anyone who enters and leaves the campus of the University of Computer Studies, Hinthada (UCSH). This
system is implemented with the 405 students in the 2022-2023 Academic Year, 86 permanent staff including
the principal whose ID card (Name, Year, Roll No, NRC, Father Name: for students, Name, Rank,
Department, NRC, Address: for teachers and staffs). As soon as someone enters the campus of a university,
the ID card is scanned, the images of the ID card are captured and the face onthe card will be matched
with the faces in the trained dataset to detect by using the Haar cascade classifier, and recognize the face
using Local Binary Pattern LBPH algorithm. The proposed system demonstrates strong performance,
achieving an accuracy rate of over 90% for everyone entering the campus. It is both effective and efficient,
providing a smart solution for identification.
Campus Management System with ID Card using Face Recognition with LBPH Algorithm
The Face recognition system mentioned is a computer vision and image processing application designed to carry out two primary functions: identifying and verifying a person from an image or a video database. The objective of this research is to provide a more efficient and effective alternative to traditional manual management systems. It can be used in offices, schools, and organizations where security is critical. In the proposed system, initially, all students enrolled in the Academic Year whose information is stored in a database server and released a unique ID Card with their facial image to be a smart campus. The main objective of the proposed system is to automate the time-in, and time-out of students, teachers, staff, and anyone who enters and leaves the campus of the University of Computer Studies, Hinthada (UCSH). This system is implemented with the 405 students in the 2022-2023 Academic Year, 86 permanent staff including the principal whose ID card (Name, Year, Roll No, NRC, Father Name: for students, Name, Rank, Department, NRC, Address: for teachers and staffs). As soon as someone enters the campus of a university, the ID card is scanned, the images of the ID card are captured and the face onthe card will be matched with the faces in the trained dataset to detect by using the Haar cascade classifier, and recognize the face using Local Binary Pattern LBPH algorithm. The proposed system demonstrates strong performance, achieving an accuracy rate of over 90% for everyone entering the campus. It is both effective and efficient, providing a smart solution for identification.
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Final PPT.pptx
1. The document proposes a project to develop a real-time face detection and security system for ATM machines that uses face recognition technology to authenticate users instead of passwords or PINs.
2. It discusses using algorithms like Haar Cascade and high resolution cameras to capture and analyze facial images to compare against an enrolled user database for authentication.
3. The project aims to provide a more convenient and secure authentication method for ATM users while enhancing security and mitigating risks of card theft or fraud compared to traditional systems.
Smart Door Access using Facial Recognition
This document summarizes a research paper that proposes a facial recognition system using a Raspberry Pi to unlock doors. It uses conventional techniques like Haar detection and PCA (principal component analysis). The system extracts features from faces and represents them as eigenfaces to encode facial identities. These eigenfaces are used as inputs to a neural network for facial classification and recognition. When a recognized face is detected, a servo motor will unlock the door automatically. If an unrecognized face is found, the system will notify an administrator. The goal is to create an efficient and affordable facial recognition access system as an alternative to keys or ID cards.
Face Recognition Based Payment Processing System
This document proposes a face recognition-based payment processing system. It uses facial recognition technology to identify users and process payments securely. The system works as follows:
1. A user's face is detected using Haar Cascade classifiers and their facial features are extracted using Local Binary Pattern Histograms (LBPH).
2. If the detected face matches a user in the system database, a one-time password (OTP) is sent to the user's registered mobile number.
3. Upon entering the correct OTP, the payment transaction is completed. Otherwise, the transaction fails.
This system aims to provide secure contactless payments using facial biometrics for identification. It avoids the need for users to carry payment
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Emphysema is a disease condition of respiratory system.
Emphysema is an abnormal permanent enlargement of the air spaces distal to terminal bronchioles, accompanied by destruction of their walls and without obvious fibrosis.
Emphysema of lung is defined as hyper inflation of the lung ais spaces due to obstruction of non respiratory bronchioles as due to loss of elasticity of alveoli.
It is a type of chronic obstructive
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1. The document proposes a project to develop a real-time face detection and security system for ATM machines that uses face recognition technology to authenticate users instead of passwords or PINs.
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Face Recognition Based Payment Processing System
This document proposes a face recognition-based payment processing system. It uses facial recognition technology to identify users and process payments securely. The system works as follows:
1. A user's face is detected using Haar Cascade classifiers and their facial features are extracted using Local Binary Pattern Histograms (LBPH).
2. If the detected face matches a user in the system database, a one-time password (OTP) is sent to the user's registered mobile number.
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Click here for more Details: https://www.connectinfosoft.com/artificial-intelligence-and-machine-learning-development-service/IRJET-Human Face Detection and Identification using Deep Metric Learning
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.
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This document is a project report submitted by Snehal Nemane to the Institute for Future Education Entrepreneurship and Leadership (iFEEL) in partial fulfillment of the requirements for a Post Graduate Diploma in Management. The report explores the topic of Artificial Intelligence in Retail. It includes a declaration by the author, a certificate signed by the project guide and dean, an acknowledgement, abstract, table of contents, and five chapters that discuss literature review on AI, company and industry profiles, research methodology, data analysis and findings.
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2. The system is designed to help prevent the spread of COVID-19 by only allowing people wearing face masks to enter certain locations. It works by detecting faces with a camera or video feed, classifying whether a mask is detected on the face using a trained model, and then sends a signal to an Arduino device that can control access points like doors.
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This document presents a study on sign language identification based on hand gestures. The researchers aim to develop a system that can recognize American Sign Language gestures from video sequences. They use two different models - a Convolutional Neural Network (CNN) to analyze the spatial features of video frames, and a Recurrent Neural Network (RNN) to analyze the temporal features across frames. The document discusses the methodology used, including data collection from videos, pre-processing of frames, feature extraction using CNN models, and gesture classification. It also provides a literature review on previous studies related to sign language recognition and communication systems for deaf people.
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Sign Language Identification based on Hand Gestures
Smart applications smart platforms, security, processes and interactions
Smart applications smart platforms, security, processes and interactions
The 10 most innovative cognitive solution providers 2018
The 10 most innovative cognitive solution providers 2018
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Emphysema is a disease condition of respiratory system.
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Furthermore, the time constraints and workload in healthcare settings can make it challenging for caregivers to prioritise safe patient handling Australia practices, leading to shortcuts and increased risks.
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Hypertension and it's role of physiotherapy in it.
This particular slides consist of- what is hypertension,what are it's causes and it's effect on body, risk factors, symptoms,complications, diagnosis and role of physiotherapy in it.
This slide is very helpful for physiotherapy students and also for other medical and healthcare students.
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Hypertension, also known as high blood pressure, is a serious medical condition that occurs when blood pressure in the body's arteries is consistently too high. Blood pressure is the force of blood pushing against the walls of blood vessels as the heart pumps it. Hypertension can increase the risk of heart disease, brain disease, kidney disease, and premature death.
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Here are some key objectives of communication with children:
Build Trust and Security:
Establish a safe and supportive environment where children feel comfortable expressing themselves.
Encourage Expression:
Enable children to articulate their thoughts, feelings, and experiences.
Promote Emotional Understanding:
Help children identify and understand their own emotions and the emotions of others.
Enhance Listening Skills:
Develop children’s ability to listen attentively and respond appropriately.
Foster Positive Relationships:
Strengthen the bond between children and caregivers, peers, and other adults.
Support Learning and Development:
Aid cognitive and language development through engaging and meaningful conversations.
Teach Social Skills:
Encourage polite, respectful, and empathetic interactions with others.
Resolve Conflicts:
Provide tools and guidance for children to handle disagreements constructively.
Encourage Independence:
Support children in making decisions and solving problems on their own.
Provide Reassurance and Comfort:
Offer comfort and understanding during times of distress or uncertainty.
Reinforce Positive Behavior:
Acknowledge and encourage positive actions and behaviors.
Guide and Educate:
Offer clear instructions and explanations to help children understand expectations and learn new concepts.
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ppt ofSMARTBRAIN of gita autonomous college
- 1. SMART BRAIN By Name-Baishnabi Bibhudarshini, Lopamudra rout, Bhagyashree Maharana Rged No-2001287337,2001287369,2001287359 Computer Science and Information Technology Guided by- Prof.Lopamudra Das GITA AUTONOMOUS COLLEGE
- 2. INTRODUCTION "Smart Brain: unlocking intelligence with every interaction." Welcome to Smart Brain, a technological marvel that seamlessly integrates the brilliance of the MERN stack with the finesse of facial recognition. In this innovative realm, users are greeted with an interface crafted for intuitive exploration, powered by React's user-centric design. The beating heart of Smart Brain lies in its cutting-edge facial recognition feature, a testament to the synergy of artificial intelligence and machine learning. Users embark on a journey where every interaction is a dance between precision and intelligence. Smart Brain is not just an application; it's an immersive gateway to a world where technology meets sophistication, offering a glimpse into the future of intelligent and interactive platforms.
- 4. About Smart Brain 01 02 : 03 A React-powered interface, ensuring a responsive and intuitive interaction for users engaging with the facial recognition feature. 04 Leveraging Node.js and Express.js, Smart Brain establishes a robust backend system, facilitating efficient server-side logic and seamless communication with MongoDB for optimal data management . The project's focal point is its cutting-edge facial recognition feature, driven by AI and machine learning, offering users an intelligent and interactive platform. Smart Brain leverages the power of MongoDB, Express.js, React, and Node.js (MERN stack) to create a dynamic web application with robust features and scalability.
- 5. STEPS Face recognition development process includes the following steps: 1.First user have to register the platform if already registred then need to sign in through email id and password. 2.User have to upload picture to detect the face and they have to click on detect. 3.Developers feed input image to the algorithm. 4.The algorithm creates an embedding for the input image. 5.The algorithm then compares the input image’s facial embedding with the embeddings of other known faces in the database 6.After comparison, it classifies familiar and unfamiliar images and detect the faces
- 6. APPLICATIONS Social media profile moderation and verification Immigration Education Reducing online banking frauding Healthcare
- 7. How the website look like? UI Login page Here you have to put page address
- 8. Functionality As you can see User Profile and Leaderboard. And this is the last section you can see the numbers.
- 9. “In conclusion, Smart Brain stands as a testament to the convergence of advanced technologies, blending the efficiency of the MERN stack with the sophistication of facial recognition. This innovative project not only redefines user interaction but also sets a new standard for intelligent and seamless experiences. As we navigate this digital era, Smart Brain emerges as a beacon of technological excellence, offering a glimpse into the limitless possibilities that arise when innovation and precision harmonize." Conclusion
- 10. THANK YOU