This repository contains Jupyter notebooks for the course Computational Perception, part of the Computer Science track at the Universitat Politècnica de València (UPV). The course covers topics such as image processing, visual systems, and pattern recognition.

A web application powered by a deep learning model built with TensorFlow and Keras that recognizes individual chess pieces from images. The project includes a Jupyter Notebook for data preparation, preprocessing, model training, and evaluation, and a Flask web application for a user-friendly image upload and prediction interface.

Image-Detection uses the powerful YOLOv5 model for real-time object detection, enabling you to identify objects like cats and dogs in images. This repository provides easy-to-follow Jupyter notebooks to get started with image recognition. It's a perfect starting point for anyone looking to dive into computer vision and deep learning.

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.