LBP-Based Face Recognition Implementation

MATLAB 2.18M 221 views 0 downloads 1 credits
Tags:

Resource Overview

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.

Detailed Documentation

We developed a face recognition system using the open-source Yale face database. The system utilizes Local Binary Patterns (LBP) to extract local facial features through texture pattern analysis, where each pixel is compared with its neighbors to generate binary codes representing local image textures. These features are then classified using the K-Nearest Neighbors (K-NN) algorithm, which calculates Euclidean distances between feature vectors to identify the closest matches in the training dataset. The system achieves approximately 90% recognition accuracy, demonstrating excellent performance. Future enhancements could include implementing deep learning algorithms like convolutional neural networks (CNNs) to further improve recognition accuracy through hierarchical feature learning. Overall, this face recognition system delivers efficient and accurate performance on the Yale database, providing a robust solution for facial identification tasks.

You May Also Like