LBP Feature Computation Program for Face Recognition

For face recognition applications presented in our research, we have developed an LBP-based feature computation program. This method generates practical statistical histogram features through a straightforward implementation process involving local binary pattern encoding and histogram aggregation. The computational procedure typically includes: 1) dividing the facial image into cells, 2) calculating LBP codes for each pixel by thresholding neighborhoods, 3) constructing cell histograms, and 4) concatenating histograms into a final feature vector. Through algorithmic improvements such as uniform pattern optimization and multi-scale analysis, we can further enhance recognition accuracy and performance. In practical applications, these LBP features can be integrated with other techniques like PCA or SVM classifiers to handle complex recognition scenarios. Our research contributes significantly to advancing face recognition methodologies by providing efficient feature extraction capabilities and implementation frameworks.