Face Recognition Using Non-Negative Matrix Factorization (NMF) with Implemented Algorithm Source Code
Non-Negative Matrix Factorization (NMF) Implementation for Face Recognition with Functional Algorithm Source Code
MATLAB Implementation of Illumination Compensation Code for Face Recognition Preprocessing
Introduction to MATLAB code implementation for illumination compensation in face recognition preprocessing with enhanced algorithm explanations
PIE FACE Database for Face Recognition in Computer Vision Applications
The PIE FACE database supports computer vision face recognition with multiple poses, varying viewpoints, and diverse illumination conditions
MATLAB Implementation of PCA Face Recognition with Complete Code Solution
This PCA face recognition program includes trained face dataset and demonstrates efficient implementation of Principal Component Analysis for face recognition. The code runs smoothly in MATLAB environment and provides high recognition accuracy through optimized feature extraction algorithms.
MATLAB Implementation of Face Recognition Database
Face Recognition Database Implementation for MATLAB Environment: CMU PIE Face Database Integration and Application
PCA-Based Face Recognition Method MATLAB Source Code
MATLAB source code for a Principal Component Analysis (PCA) based face recognition system - PCA_based Face Recognition System.rar, featuring dimensionality reduction and feature extraction algorithms
2DPCA Algorithm for Face Recognition with MATLAB Implementation
MATLAB implementation of the 2DPCA algorithm for face recognition utilizing nearest neighbor classifier, featuring image feature extraction and similarity-based classification
MATLAB Implementation of 2DPCA Algorithm with Face Recognition Application
Implementation of 2DPCA algorithm in MATLAB using ORL face database, demonstrating high recognition accuracy through efficient feature extraction and dimensionality reduction techniques
LBP-Based Face Recognition Implementation
Face recognition system implemented using the open-source Yale face database, employing Local Binary Patterns (LBP) for facial feature extraction and K-Nearest Neighbors algorithm for classifying facial feature vectors, achieving approximately 90% recognition accuracy with excellent performance results.
LVQ Neural Network for Prediction - Face Orientation Recognition
Face recognition, as a complex pattern recognition problem, has garnered widespread attention in recent years, with various methods in the recognition field demonstrating their strengths and leading to the development of many novel approaches that significantly enrich and broaden the direction of pattern recognition. This project utilizes an image database containing facial images from different angles, comprising 10 individuals with 5 images each, depicting face orientations: left, left-front, front, right-front, and right. An LVQ neural network is created to predict and recognize the orientation of any given facial image. Compared to BP neural networks, LVQ networks require no data preprocessing and directly compute distances between input vectors and competitive layers for pattern recognition. Recent years have seen increasingly in-depth research on LVQ neural networks, with applications becoming more widespread.