Face Detection Using Skin Color Segmentation and SVM

This documentation provides a MATLAB implementation for face detection utilizing skin color segmentation and SVM classification. The core algorithm employs YCgCr color space for skin tone modeling, where specific chrominance components (Cg and Cr) are thresholded to isolate potential facial regions. The segmented regions then undergo feature extraction and are validated using a pre-trained SVM classifier to distinguish genuine faces from false positives. The implementation consists of two main phases: First, color-based segmentation using empirically determined thresholds in YCgCr space to capture skin-like pixels. Second, machine learning verification where HOG (Histogram of Oriented Gradients) features are extracted from candidate regions and classified using an SVM model trained on facial characteristics. This approach is particularly suitable for developing face recognition systems in security monitoring and video surveillance applications. The modular code structure allows customization of color thresholds, feature extraction parameters, and SVM training data to adapt to specific lighting conditions and ethnic variations. Researchers and developers can modify the threshold values, implement different feature descriptors, or retrain the SVM classifier to optimize performance for their particular use case. Key functions include: - rgb2ycgcr() for color space conversion - skinMask = (Cg > threshold1) & (Cr > threshold2) for skin region identification - extractHOGFeatures() for feature vector generation - predict(svmModel, features) for classification We hope this implementation provides valuable insights for your computer vision projects and serves as a foundation for further development in biometric systems and intelligent video analysis.