Audio fingerprinting and recognition in Python

Fingerprint recognition with OpenCV

This project implements a fingerprint recognition system using ORB for feature extraction and SVM for classification in Python. It preprocesses fingerprint images, trains a machine learning model, and predicts the person ID from new inputs. The system is designed for beginners to learn biometric authentication and image processing.

Quad-based audio fingerprinting and recognition in Python

Audio Fingerprinting and Recognition in Python using NVidia's CUDA

Fingerprint recognition in Python

Python extension for Nitgen fingerprint recognition devices

Fingerprint recognition using Python

Fingerprint Recognition Using Harris Corner Detector in Python

Docker container for dejavu: Audio fingerprinting and recognition in Python

Music sound recognition, matching and fingerprinting with Python

Fingerprint recognition using Python, including pattern-oriented matching, correlation-oriented matching

A music recognition program, which uses FFT and Python's scientific modules to create and distinguish audio fingerprints of songs. Encapsulated and Abstract, the module is plug and play, in this repo I displayed the results at a website I created for this purpose

dejavu on docker: Audio fingerprinting and recognition in Python – https://github.com/worldveil/dejavu

Diploma project which implements fingerprint recognition using Python and doesn't include OpenCV.

No description available

🔥🔥🔥Python fingerprint recognition指纹识别系统

Algorithm and python code for Acoustic fingerprinting and song recognition

Shazam-style audio recognition system in Python using spectral peak hashing and time-offset matching. Ingest songs from YouTube, fingerprint them, and identify them from short microphone recordings.

In this digital era, face recognition system plays a vital role in almost every sector. Face recognition is one of the mostly used biometrics. It can used for security, authentication, identification, and has got many more advantages. Despite of having low accuracy when compared to iris recognition and fingerprint recognition, it is being widely used due to its contactless and non-invasive process. Furthermore, face recognition system can also be used for attendance marking in schools, colleges, offices, etc. This system aims to build a class attendance system which uses the concept of face recognition as existing manual attendance system is time consuming and cumbersome to maintain. And there may be chances of proxy attendance. Thus, the need for this system increases. This system consists of four phases- dataset creation, face detection, face recognition, attendance updating. Dataset is created by the images of the students in class. The system is designed in TKINTER platform for GUI supported with a script of PYTHON as well as SQL database. Face detection and recognition is performed using Haar-Cascade classifier and Local Binary Pattern Histogram algorithm respectively. Faces are detected and recognized from live streaming video of the classroom. The system has output in the form of excel sheet.

Nhận Dạng Hình Ảnh Vân tay, (Fingerprint image recognition) bằng thuật toán CNNs

Shazam-inspired audio recognition app using audio fingerprinting, built with Android and Python

Python fingerprint recognition using SVM

Audio fingerprinting and recognition in Python

🔥🔥🔥Python+Django+HTMLl网页前后端指纹信息识别 web page front-end and back-end fingerprint information recognition

🔥🔥🔥python指纹录入考勤管理系统fingerprint input recognition attendance management system

fingerprint recognition python 50 threshold opencv only

Implemented Fingerprint recognition System in python using *minutiae matching technique*.

Shazam's Clone Algorithm (From Scratch). An audio fingerprinting and recognition system built from scratch in C and Python, the same core algorithm behind Shazam.

Complete educational AFIS (Automated Fingerprint Identification System) built in Python. Implements the full fingerprint recognition pipeline: acquisition, preprocessing (Gabor filters, skeletonization), minutiae extraction, template storage (SQLite), and matching (1:1 & 1:N). Perfect for learning biometrics and computer vision.