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vinkCodesEXE / repository
The aim of my project falls under image classification as it attempts to match images. The algorithm takes image inputs from the user and then cycles through images stored in its database and try to predict probabilities that these faces match. This algorithm has a multitude of uses depending upon the need. For example, the algorithm can be implemented by law enforcement to match CCTV footage with potential perpetrators, either to check for past offenders or as proof during a trial. It could similarly be used by companies when hiring people to check if they have a criminal record. The inspiration for my project arose from my frustration with the increase in identity fraud and the lack of solutions for it. When companies hire people, their attempts to conduct a thorough background check can be manipulated very easily. To solve such issues, I came up with the idea to build a database which stores the image of past offenders which can be used to find criminal records of individuals.The algorithm was built using a jupyter notebook and python programming language. I used a variety of libraries like numpy, tensorflow and cv2 to construct the algorithm. The algorithm was built over a period of two months during which my mentor and I worked on the algorithm and discussed various options on methods of improvement. To train the algorithm I built a celebrity image bank because of the ease of finding pictures at different angles. In my training set, I’ve used five images of twenty different celebrities at different angles and time periods. The first step was to build the framework of the neural network, where I specified an optimization function, the number of hidden layers and a maximum iteration. The algorithm begins by storing the image information from the database in a pixel array for colored photographs. The image is then resized to suit the algorithm As the pixel info of the images is quite memory intensive, I decided to resize down to 250*250 pixels and standardize the pixel rgb values .The next step is to train the algorithm, which I have done by calibrating its input layer to accept image pixel vectors, and then providing the algorithm instructions for the multiple layers of processing i.e. learning or looking for repeated pixel patterns from the stored images, a probability vector is outputted. This probability vector calculates the ‘similarity’ of the input image that matches each celebrity in the database.The purpose of this algorithm was to control crime based on identity theft, identity fraud and help the law enforcement departments to capture criminals. As the output is a probability vector, even if they used a disguise etc, the algorithm’s prediction would not be extremely compromised.
The aim of my project falls under image classification as it attempts to match images. The algorithm takes image inputs from the user and then cycles through images stored in its database and try to predict probabilities that these faces match. This algorithm has a multitude of uses depending upon the need. For example, the algorithm can be implemented by law enforcement to match CCTV footage with potential perpetrators, either to check for past offenders or as proof during a trial. It could similarly be used by companies when hiring people to check if they have a criminal record. The inspiration for my project arose from my frustration with the increase in identity fraud and the lack of solutions for it. When companies hire people, their attempts to conduct a thorough background check can be manipulated very easily. To solve such issues, I came up with the idea to build a database which stores the image of past offenders which can be used to find criminal records of individuals.The algorithm was built using a jupyter notebook and python programming language. I used a variety of libraries like numpy, tensorflow and cv2 to construct the algorithm. The algorithm was built over a period of two months during which my mentor and I worked on the algorithm and discussed various options on methods of improvement. To train the algorithm I built a celebrity image bank because of the ease of finding pictures at different angles. In my training set, I’ve used five images of twenty different celebrities at different angles and time periods. The first step was to build the framework of the neural network, where I specified an optimization function, the number of hidden layers and a maximum iteration. The algorithm begins by storing the image information from the database in a pixel array for colored photographs. The image is then resized to suit the algorithm As the pixel info of the images is quite memory intensive, I decided to resize down to 250*250 pixels and standardize the pixel rgb values .The next step is to train the algorithm, which I have done by calibrating its input layer to accept image pixel vectors, and then providing the algorithm instructions for the multiple layers of processing i.e. learning or looking for repeated pixel patterns from the stored images, a probability vector is outputted. This probability vector calculates the ‘similarity’ of the input image that matches each celebrity in the database.The purpose of this algorithm was to control crime based on identity theft, identity fraud and help the law enforcement departments to capture criminals. As the output is a probability vector, even if they used a disguise etc, the algorithm’s prediction would not be extremely compromised.
Selected from shared topics, language and repository description—not editorial ratings.
HenryHengLUO /
This project aims to introduce and demonstrate the practical applications of RAG using Python code in a Jupyter Notebook environment.
computationalcore /
A very useful collection of Jupyter Notebooks, which aims to introduce the Python programming language.
Devtown-India /
Over the past decade, bicycle-sharing systems have been growing in number and popularity in cities across the world. Bicycle-sharing systems allow users to rent bicycles on a very short-term basis for a price. This allows people to borrow a bike from point A and return it at point B, though they can also return it to the same location if they'd like to just go for a ride. Regardless, each bike can serve several users per day. Thanks to the rise in information technologies, it is easy for a user of the system to access a dock within the system to unlock or return bicycles. These technologies also provide a wealth of data that can be used to explore how these bike-sharing systems are used. In this project, you will use data provided by Motivate, a bike share system provider for many major cities in the United States, to uncover bike share usage patterns. You will compare the system usage between three large cities: Chicago, New York City, and Washington, DC. Day:1 In this project, Students will make use of Python to explore data related to bike share systems for three major cities in the United States—Chicago, New York City, and Washington. You will write code to import the data and answer interesting questions about it by computing descriptive statistics. They will also write a script that takes in raw input to create an interactive experience in the terminal to present these statistics. Technologies that will be covered are Numpy, Pandas, Matplotlib, Seaborn, Jupyter notebook. We will be giving the students a deep dive into the Data Analytical process Day:2 We will be giving the students an insight into one of the major fields of Machine Learning ie. Time Series forcasting we will be taking them through the relevant theory and make them understand of the importance and different techniques that are available to deal with it. After that we will be working hands on the bike share data set implementing different algorithms and understanding them to the core We aim to provide students an insight into what exactly is the job of a data analyst and get them familiarise to how does the entire data analysis process work. The session will be hosted by Shaurya Sinha a data analyst at Jio and Parag Mittal Software engineer at Microsoft.
Facial recognition could soon jump from your smartphone to your workplace with employers using it to mark attendance and gauge the mood of the workforce.Every day, corporate offices and institutes are working to increase the productive working hours in a day. When the current system of clocking in daily using a fingerprint scanner is a time-consuming and inefficient use of time. I have planned to design a Voice Interactive Face Detection Based Smart Attendance management and behavior analysis to ensure a better work culture and environment,efficiency in a secure manner using Intel dev cloud. Currently, we have fingerprint and Smart-card Based entries in nearly all offices and a few schools and colleges. These system then automates the calculation of salary or attendance percentage.But fingerprint scanning and smart card barcode entries tend to take up time and prove to also be imperfect. In contrast, Face Recognition method provides a unique feature for every individual which is stored in a central database and can be retrieved during recognition and validation. The system includes an embedded application deployed in a SCB( Single Board Computer) which can interact with the users in real time. It will take down in and out time of every employee and monitor their working behavior(future scope) and notify the corresponding employee and the authority at times. We are aiming to analyze people's behavior,mood and emotions by monitoring and studying their actions in real time which in turn will help the organization know about the physical and mental status of the employees. This process of direct integration of physical world into computer vision based systems will indeed result in efficiency improvements, economic benefits and reduced human exertions. As of now I have developed a basic voice interactive attendance monitoring using Jupyter Notebook on Intel dev cloud. The in and out time (including mid in and out) will be monitored in Google spreadsheet and the system will calculate how many hours an employee has spent in office premises. The system won’t allow employees to step into the office after a certain time and won’t consider the attendance if the total hours spent is less than four hours. Everyday a mail will be sent to the admin containing the attendance details of the employees. In future, I would like to implement behavior and mood analysis of the employees and the staff on the office premises which in turn will help the concerned staff provide with solutions to get over the listless mood or erratic behavior.
SiddheshBangar /
The "Learn-Machine-Learning" repository on GitHub is a collection of resources and code examples aimed at helping beginners learn the basics of machine learning. The repository includes various Jupyter notebooks and Python scripts that cover topics such as data preprocessing, regression, classification and clustering.
DiGyt /
This repository aims to introduce students at the University of Osnabrück to Neurodynamics, allowing them to interactively investigate several neurodynamic processes using Jupyter Notebooks in Google Colab.