Facial lock pipeline along with a helper REST-ful flask API for the same

Attendance Using Face Recognisation

Real-time missing person detection system using facial recognition and SQLite database.

Docker and Flask based API layer + data ingestion pipeline for the Facenet-PyTorch facial recognition library. I.e. simple ML deployment for matching pairs of photos

Classification of Sober and Intoxicated Faces using Image Analysis

Face Recognition system trained for 81 people. Accuracy is around 70% achieved.

A face recognition model build with an ensemble of popular pre-trained models like FaceNet and OpenFace, on training with a dataset of 31 celebrity images. Built an application which can recognise a new person based on stored embedding of him and relate his facial features to the 31 celebrities it was trained.

A Gradio-based web application that detects whether an image is a deepfake. The application uses a pre-trained InceptionResnetV1 model from the facenet_pytorch library for face recognition, and pytorch-grad-cam for visual explainability.

A discriminative few-shot learning approach for face recognition and verification using a Siamese network architecture. Employing a triplet loss function, the model optimizes the embedding space to cluster faces of the same individual and separate those of different individuals, enhancing accuracy and efficiency with limited training data.

Real-Time Face Identification using Convolutional Neural Networks

TubeFaceCrop is a tool for crawling videos related to keywords from YouTube and preprocessing them. It is based on MTCNN to automatically remove faces from the videos and perform central crop, and finally segments the videos into 5-second clips for further processing and analysis.

Recognition of Iranian human facial emotions across four categories: Happy, Sad, Angry, and Neutral.

🎬 VideoDeepFakeDetection uses AI to authenticate videos through a multi-step process, identifying potential deepfakes for enhanced content reliability.

This project is an implementation of a secure and efficient voting system utilizing facial recognition technology. The system aims to enhance the integrity and convenience of the voting process by allowing voters to authenticate themselves through facial recognition before casting their votes.

This repository employs TensorFlow-Keras and MTCNN (Multi-Task Cascaded Convolutional Networks) to build an efficient deepfake detection system.

Developed a working prototype of a University Security Monitoring System using Deep Learning to assist Reva University in maintaining a database of known and unknown persons entering through the entrance gate by detecting the frontal faces of students.

This repository hosts a cutting-edge facial recognition system designed to enhance customer identification and verification. Leveraging MTCNN for accurate face detection and DeepFace-FaceNet for facial embeddings, the system integrates with Pinecone's vector database to efficiently match and verify repeat customers.

Application for Innopolis University to increase speed of checking attendance in events

This repository contains different algorithms and methods to anonymize faces in images by blurring or pixelating them using OpenCV and MTCNN in Python