Gabor Wavelet Transform for Facial Expression Recognition with Multi-Scale and Multi-Orientation Feature Extraction

The Gabor Wavelet Transform is a widely adopted method for facial expression recognition, which processes facial images to extract multi-scale and multi-orientation features. These features serve as fundamental components for applications like facial recognition and expression classification. In this implementation, we employ Gabor wavelet transformation combined with an enhanced Fisher Linear Discriminant (FLD) model to improve recognition accuracy.

Technically, the algorithm constructs Gabor wavelet kernel functions across 5 scales and 8 orientations. Through convolutional operations with input images, the code generates a discriminative feature set by applying these Gabor filters at different frequencies and angles. Key implementation steps include: 1) Preprocessing facial images (normalization/resizing), 2) Creating Gabor filter banks with varying wavelengths (scales) and orientations, 3) Performing 2D convolution to extract texture features, and 4) Applying enhanced FLD for dimensionality reduction and classification.

This program references the methodology described in "Gabor feature classification using enhanced FLD model facial recognition," with optimizations including improved filter parameter selection and computational efficiency. The resulting features enable effective identification and categorization of facial expressions, providing a robust foundation for advanced research and practical applications in affective computing.