MATLAB Face Recognition Attendance System Using PCA Algorithm

Section 1: Project Introduction This project implements a PCA-based face recognition attendance system on the MATLAB platform. Unlike traditional face recognition methods that simply compare head-to-head images - which has limited practical application and has become oversaturated in academic projects - this system introduces an innovative approach. The methodology involves detecting faces from life photographs, segmenting facial regions, applying Principal Component Analysis (PCA) for dimensionality reduction, and comparing processed images against a database to identify target faces while recording recognition timestamps. The system additionally calculates monthly attendance frequency and computes attendance rates. Key implementation aspects include: - Face detection using Viola-Jones algorithm through MATLAB's vision.CascadeObjectDetector - Image segmentation employing region-based methods for precise facial extraction - PCA implementation using MATLAB's pca() function for efficient feature reduction - Database matching through Euclidean distance calculations in reduced dimension space The project allows for secondary development including stranger recognition (non-database faces) with alarm functionality. The GUI interface provides data clearance features and payroll statistics capabilities. Section 2: Basic Workflow The system follows a structured pipeline: 1. Image Input: Reading images using imread() function with format validation 2. Face Localization: Applying Haar-like features for face detection and bounding box generation 3. Face Segmentation: Extracting facial regions using mask operations and morphological processing 4. Face Recognition: Implementing PCA transformation and similarity measurement against database templates 5. Security Alert: Optional module for unauthorized face detection with alert triggering mechanism