MATLAB Code Implementation for Face Recognition
Face recognition source code implementation featuring face detection, recognition components, and associated algorithm descriptions
Face Detection Using the Adaboost Algorithm
Implementing face detection with the Adaboost algorithm, a powerful machine learning approach that combines multiple weak classifiers to create a robust detector capable of accurately identifying faces in images
Wavelet Neural Network Face Detection Source Code
High-quality wavelet neural network face detection source code implementation with advanced algorithm architecture
Face Detection Implementation Tool for MATLAB
A comprehensive MATLAB-based toolkit for face detection implementation with optimized algorithms and practical examples
Eye Localization and Face Detection
A comprehensive program for eye localization and face detection, including both executable code and simulation components, delivering excellent performance results!
AdaBoost Face Detection DLL Module using OpenCV with MATLAB Integration
This MATLAB-based implementation utilizes an OpenCV-built AdaBoost face detection DLL module to perform real-time face detection on video streams captured from USB cameras. The system displays detection results in real-time, though with slightly lower performance compared to native C implementations. After downloading, users can run the module directly in MATLAB without modifying relative file positions within the folder structure.
Debugged Algorithm for Eye Localization and Face Detection
This code implements a debugged algorithm for precise eye localization and robust face detection.
Face Detection Implementation
Face Detection using MATLAB Programming; Implementing Rapid Object Detection with Boosted Cascade of Simple Features Algorithm
Feature Extraction using Gabor Wavelet Filters followed by SVM Classification for Face Detection
This implementation performs feature extraction using Gabor wavelet filters, then employs Support Vector Machine (SVM) classification for face detection. The code requires MATLAB 2010 or later versions for execution, utilizing MATLAB's image processing toolbox for filter implementation and SVM functions for pattern classification.
MATLAB Code for Face Detection with Viola-Jones Algorithm Implementation
Application Context: Face detection code demonstrating how to detect faces, noses, mouths, and eyes using MATLAB's built-in classes and functions. Based on the Viola-Jones face detection algorithm, the Computer Vision System Toolbox includes the vision.CascadeObjectDetector system for object detection. Prerequisites: Computer Vision System Toolbox must be installed. Key Technology: MATLAB enables face detection through various techniques including boundary setting, edge detection, and utilizing signal processing and image processing tools. This technology serves security purposes by allowing authorized personnel access through comparison with pre-stored facial data.