This document describes a project to implement real-time facial recognition using OpenCV and Python. The project uses a laptop's webcam to capture video frames and detect and recognize faces in each frame. It trains an image dataset with face images and IDs then detects faces in each new video frame. It predicts faces by comparing features to the training data and labels matches based on a confidence level threshold. The document outlines the use of Haar cascade classifiers, LBPH algorithms, and OpenCV functions to complete the facial recognition process in real-time on new video frames from the webcam.
Presentation for the Berlin Computer Vision Group, December 2020 on deep learning methods for image segmentation: Instance segmentation, semantic segmentation, and panoptic segmentation.
Face detection basedon image processing by using the segmentation methods for detection of the various types of the faces to helpfull for the many different careers and it will easy to do.
Face recognition is a biometric technology that goes beyond just detecting human faces in an image or video. It goes a bit further to determine whose face it is. A face recognition system works by taking an image of a face and predicting whether the face matches another face stored in a dataset (or whether a face in one image matches a face in another). Created By Suman Ahemed Saikan
Presentation for the Berlin Computer Vision Group, December 2020 on deep learning methods for image segmentation: Instance segmentation, semantic segmentation, and panoptic segmentation.
Face detection basedon image processing by using the segmentation methods for detection of the various types of the faces to helpfull for the many different careers and it will easy to do.
Face recognition is a biometric technology that goes beyond just detecting human faces in an image or video. It goes a bit further to determine whose face it is. A face recognition system works by taking an image of a face and predicting whether the face matches another face stored in a dataset (or whether a face in one image matches a face in another). Created By Suman Ahemed Saikan
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
Attendance Management System using Face RecognitionNanditaDutta4
The project ppt presentation is made for the academic session for the completion of the work from Bharati Vidyapeeth Deemed University(IMED) MCA department
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. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.
Humans often use faces to recognize individuals, and advancements in computing capability over the past few decades now enable similar recognitions automatically. Early facial recognition algorithms used simple geometric models, but the recognition process has now matured into a science of sophisticated mathematical representations and matching processes. Major advancements and initiatives in the past 10 to 15 years have propelled facial recognition technology into the spotlight. Facial recognition can be used for both verification and identification.
A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source.
This slide is all about a detailed description of the Face Recognition System.
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
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
Attendance Management System using Face RecognitionNanditaDutta4
The project ppt presentation is made for the academic session for the completion of the work from Bharati Vidyapeeth Deemed University(IMED) MCA department
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. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.
Humans often use faces to recognize individuals, and advancements in computing capability over the past few decades now enable similar recognitions automatically. Early facial recognition algorithms used simple geometric models, but the recognition process has now matured into a science of sophisticated mathematical representations and matching processes. Major advancements and initiatives in the past 10 to 15 years have propelled facial recognition technology into the spotlight. Facial recognition can be used for both verification and identification.
A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source.
This slide is all about a detailed description of the Face Recognition System.
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
The goal of this report is the presentation of our biometry and security course’s project: Face recognition for Labeled Faces in the Wild dataset using Convolutional Neural Network technology with Graphlab Framework.
Decomposing image generation into layout priction and conditional synthesisNaeem Shehzad
in this presentation you can learn how to decompose an image into layout and find the predictions. In this presentation , I mention all the data in very convenient way , I hope you can take it easy.
Thank you.
This is the Bangla Handwritten Digit Recognition Report. you can see this report for your helping hand.
**Bengali is the world's fifth most spoken language, with 265 million native and non-native speakers accounting for 4% of the global population.
**Despite the large number of Bengali speakers, very little research has been conducted on Bangali handwritten digit recognition.
**The application of the BHwDR system is wide from postal code digit recognition to license plate recognition, digit recognition in cheques in the banking system to exam paper registration number recognition.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
1. Real Time Facial
Recognition using
Open CV and
Python
Master’s EMM – Computer Vision Project
👦
Presented By:
• Akash Satamkar
• Krupali Rana
(31672)
(31668)
(using Laptop’s Webcam)
Under guidance of : Prof. Dr. StefanElser.
2. Introductio
n
• Implementation of Face Detection and Recognition by simply
using Laptop’s Webcam.
• Real Time face detection using Face Detection algorithm to
visualize human faces in Digital image.
• We are using video feed from a webcam which are a sequence of
frames of still images being updated one after the other to
recognize and predict faces.
2
3. Goal
s
• To train the image dataset and store them with proper
faceID.
• Passing the frames captured by webcam one by one to
detect faces.
• Depending upon the confidence level, determine
whether to label the predicted face or not.
• Validating the test results with multiple test cases.
• Improving the training data with larger data sets of
images.
3
5. Haar Cascade
Classifier
5
▪ Haar feature based classifier is a machine learning
based approach
▪ Detect objects inimages
▪ Train a lot of Positive and negative images
▪ The haarcascade_frontalface_default.xml is a haar
cascade designed by OpenCV to detect the frontal
face
6. Positive and Negative
images
▪ Positive face example images provide a lot of variations. It
manually crops and normalize each face into a standard
size
▪ Negative non face examples are images that don’t contain
faces. They are taken from arbitrary images which do not
contain the object you want to detect.
Positive face set of images
Negative non face set of images
6
7. Computation of Features
detectMultiscale() Module
- To create rectangle around thefaces
detected in image.
Parameters :
scaleFactor = Adjust the size of image
minNeighbors = Specify how many
neighbors person can have
Cascade of Classifiers
Features are grouped together intostages
of classifiers.
If a window fails at 1st stage , it is discarded.
Else it is passed to 2nd stage of features.
The window which passes all stages is face
region.
7
8. LBPH Algorithm
• Local Binary Pattern is simple but
efficient texture operator.
• It is combined with HOG (Histogram of
Gradients) to recognize faces in image.
It uses 4 parameters : {Radius, Neighbours, Grid X, Grid Y}
LBP Operation:
It uses sliding window concept basedon
parameters Radius and Neighbours.
8
9. • Extracting a portion of this grayscale image (3 x 3 pixels)
• Represented in a matrix of 3 x 3 of pixel intensities with each pixel intensity in range (0 – 255)
• Using centre value as threshold and perform thresholding. (0 = < threshold, 1 = > threshold)
• Obtain binary values and concatenate in clockwise manner.
• Convert binary value to decimal value and set it to centre value.
• In the end , we have obtained a new image with better characteristics.
9
Image Reference : https://towardsdatascience.com/face-recognition-how-lbph-works-90ec258c3d6b
10. LBP combined with Histogram to predict faces:
10
Image Reference : https://towardsdatascience.com/face-recognition-how-lbph-works-90ec258c3d6b
• Now we divide the new image generated into grids with Grid X and Grid Y parameters.
• Obtain the histogram of each grid .
• Now concatenate the individual histograms to obtain a new and bigger histogram.
• The final histogram represents characteristics of original image.
11. • The algorithm is trained and each histogram is used to represent each image of training
dataset.
• We compare the two histograms and return the image with closest histogram.
• The output is the ID with closest match and the calculated Euclidean distance can be a
confidence measurement.
▪ Confidence level :
The lower the value of confidence the better is the match which means the distance
between two histograms is closer.
Then we can use this confidence level to predict the face by defining the threshold.
This method is illumination invariant in nature.
Robust method to represent local features in image
11
14. Our Process is easy
Creating Dataset
Creating two subdirectories
and loading them with set of
individual images for
training.
Training Dataset
Training the dataset
using LBPH algorithm
and save the trained
data into “.yml” file.
Predicting Facesin
Real time
Loading the trained data
file and predicting faces
frame by frame and label
them.
14
23. Further
Enhancements
• The training dataset can be improved by including
more no. of sample images.
• We can further implement CNN (Convolution Neural
Networks) using TensorFlow for better Face
Recognition.
23
24. References
▪ Working of LBPH : https://towardsdatascience.com/face-recognition-how-lbph-works-
90ec258c3d6b
▪ Article on face detection basics : https://www.datacamp.com/community/tutorials/face-
detection-python-opencv
▪ Link for downloading Anaconda package - https://www.anaconda.com/distribution/
▪ Link for Basics of Haar Cascade classifier-
https://docs.opencv.org/3.4.1/d7/d8b/tutorial_py_face_detection.html
▪ https://www.learnopencv.com/face-detection-opencv-dlib-and-deep-learning-c-python/
24