Face Detection Using Gabor Feature Extraction and Neural Networks

In this implementation, we employ a face detection methodology based on Gabor feature extraction and neural networks. The Gabor filter bank implementation extracts multi-scale and multi-orientation facial textures by convolving input images with complex Gabor kernels at different frequencies and angles. These biologically-inspired features effectively capture facial patterns while maintaining illumination invariance. The extracted features are then fed into a neural network classifier (typically implemented with layers like Conv2D, ReLU, and Softmax activation) trained to distinguish facial regions from non-facial areas through backpropagation optimization. To enhance detection performance, we integrate additional techniques including non-maximum suppression for bounding box refinement, histogram equalization for contrast normalization, and data augmentation strategies during training. The system achieves robust face localization through sliding window scanning across multiple image scales, with post-processing steps eliminating duplicate detections. This implementation serves as a comprehensive reference for understanding and applying modern face detection techniques, demonstrating practical integration of feature engineering and deep learning components.