OpenAFIS: High performance C++ fingerprint matching library
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Updated
Feb 12, 2022 - C++
OpenAFIS: High performance C++ fingerprint matching library
A system for identifying latent fingerprints. Created at Michigan State University by Anil K. Jain, Kai Cao, Dinh-Luan Nguyen, and Cori Tymoszek.
This project takes in an image of a finger, then preprocesses this input image to extract the fingerprint and using SIFT, checks if this fingerprint is already present in the database.
This project presents a fingerprint matching system utilizing deep learning. It features multiple models, including VGG-based, SENet, CBAM, Self-Attention, and Dual-Attention architectures. Pre-trained MobileNet models with Self-Attention and SENet are also included. A web application allows for easy demonstration of the model's capabilities.
In this Project we build fingerprint matching system that leverages a Siamese network to achieve accurate and efficient Fingerprint identification. The system consists of three main stages: image preprocessing, feature extraction, and matching.
Fingerprint recognition with OpenCV
Fingerprint minutiae extraction + ORB descriptor: A new method for fingerprint matching
Fingerprint Matching WinForms Application using minutiae features.
Contactless Fingerprint Authentication System for UIDAI SITAA Challenge. Implements Quality Assessment (Track A), Deep Learning Matching (Track C), and Liveness Detection (Track D). Built with TensorFlow, FastAPI, React Native. Production-ready APIs deployed on GCP.
A fingerprint matching network that treats fingerprint matching as a quadratic assignment problem. Each pore in a fingerprint is treated as a node in a graph. This model utilizes message passing, Graph COnvolution Networks, attention-based top-k network to estimate the most probable fingerprint correspondences
CSL7360 Course Project Repository
🖐️ Enable seamless identity verification with contactless fingerprint authentication, enhancing security and convenience for a smarter future.
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