Face Detection, Feature Extraction, and Face Recognition

Face detection, feature extraction, and face recognition represent core technologies in computer vision, extensively applied in security systems, identity verification, and related scenarios. Using the Yale Face Database as an example, this classic dataset contains facial images with varying lighting conditions and expressions, providing ideal samples for algorithm validation.

In technical implementation, the process begins with face detection to locate facial regions within images. Traditional approaches may rely on Haar features or HOG (Histogram of Oriented Gradients), while modern deep learning methods like MTCNN (Multi-task Cascaded Convolutional Neural Networks) achieve higher accuracy in handling complex backgrounds. MATLAB implementation typically involves using vision.CascadeObjectDetector for Haar-based detection or integrating third-party deep learning frameworks for MTCNN.

The feature extraction stage commonly employs Principal Component Analysis (PCA) for dimensionality reduction, transforming high-dimensional pixel data into distinguishable low-dimensional feature vectors known as "Eigenfaces." This method effectively compresses data while preserving crucial identification information. In MATLAB, this can be implemented using pca() function from the Statistics and Machine Learning Toolbox, followed by projecting facial images onto the principal component space to generate feature vectors.

For the final face recognition phase, Support Vector Machines (SVM) serve as a classical choice due to their excellent classification performance. By constructing optimal hyperplanes, SVM can achieve high-precision differentiation between different facial feature vectors, particularly excelling on medium-to-small scale datasets like Yale. MATLAB implementation typically utilizes fitcsvm() function for SVM model training and predict() for classification, with options for kernel selection (linear, polynomial, or RBF) to handle various data distributions. The entire workflow can be efficiently implemented using MATLAB's Image Processing Toolbox and statistical tools, providing researchers with a convenient prototyping environment.