One-Class Support Vector Machine for Face Detection

MATLAB source code for face detection using One-Class Support Vector Machine (OCSVM). This implementation utilizes SVM algorithms for face detection, where SVM represents an effective machine learning method commonly applied to classification and regression problems. OCSVM specifically refers to One-Class Support Vector Machine, a specialized variant of SVM designed for anomaly detection and outlier identification. The MATLAB implementation employs kernel functions and optimization techniques to create a decision boundary that separates face patterns from non-face patterns in image data. The source code includes essential components such as feature extraction from facial images, kernel parameter selection, and training procedures that optimize the hyperplane margin. Key functions likely involve data preprocessing, kernel matrix computation using methods like RBF (Radial Basis Function), and classification decision functions. The implementation demonstrates how to handle image datasets, extract HOG (Histogram of Oriented Gradients) or LBP (Local Binary Patterns) features, and train the OCSVM model to recognize facial patterns while rejecting background elements. Through this MATLAB implementation, users can gain practical understanding of applying support vector machine algorithms in computer vision tasks, particularly for face detection scenarios. The code provides insights into image preprocessing techniques, feature engineering approaches, and the mathematical foundations of one-class classification problems. This serves as an educational resource for learning both SVM theory and practical MATLAB programming for image analysis applications.