MATLAB Code Implementation for Face Recognition and Related Applications
Designed for image processing, face recognition, and computer vision tasks with practical code implementation examples
Face Recognition Using Hidden Markov Models
Hidden Markov Model (HMM)-based face recognition achieves an impressive 90% accuracy rate through probabilistic pattern analysis and sequential feature processing.
Eigenface-Based Face Recognition MATLAB Program
A MATLAB implementation of eigenface-based face recognition with comprehensive Chinese annotations, featuring PCA dimensionality reduction, feature extraction, and threshold-based classification algorithms.
PCA Dimensionality Reduction Followed by LDA Classification for Face Recognition
This implementation includes two core modules (PCA and LDA) that perform principal component analysis for dimensionality reduction followed by linear discriminant analysis for classification, achieving excellent experimental results in face recognition applications.
MATLAB Code Implementation for Face Recognition
Face Recognition System: PCA Feature Extraction Followed by Neural Network Classification
Face Detection Based on Skin Color Recognition Method
The system consists of the following components: capturing images using a computer's built-in camera, face detection, storing detected face images in a database, and performing face recognition using input photos. This program implements face detection through a skin color recognition approach, where the facial skin color range is defined as 100≤B≤120 and 140≤R≤160. Pixels within this range are set to white while the remaining pixels are set to black. The algorithm employs the imerode function for spherical erosion and applies median filtering to achieve smoothing effects. Finally, after scaling, binarization, and various processing stages, regions with fewer than 1000 white pixels are discarded. Image segmentation incorporates Euler numbers to eliminate background areas resembling facial colors.
MATLAB Implementation of Face Recognition Using Neural Networks
Face Recognition System Based on Neural Network with Concise Code Implementation. Key Functions: NEWFF - Creates a new feed-forward neural network, TRAIN - Trains the BP neural network, SIM - Simulates the BP neural network performance. The code demonstrates efficient neural network architecture for pattern recognition tasks.
Discrete Wavelet Transform Followed by Principal Component Analysis for Pattern Recognition Applications
This approach employs Discrete Wavelet Transform (DWT) for feature extraction and Principal Component Analysis (PCA) for dimensionality reduction, applicable to pattern recognition systems including facial recognition, palmprint analysis, emotion detection, and fingerprint identification.
MATLAB Implementation of Face Recognition Using Viola-Jones Algorithm
A classic MATLAB implementation of face recognition based on the Viola-Jones algorithm, featuring Haar-like features and AdaBoost classifier integration.
YALE Face Database: A Comprehensive Toolkit for Face Recognition Research
YALE Face Database contains 15 subjects with 11 facial images per person, providing robust data for developing and testing face recognition algorithms through various lighting conditions and expressions.