Robust Facial Recognition Using Unified Optimised

This study presents a robust face recognition method combining HDG-HOG features.

Face recognition is a cornerstone of smart system development and remains a challenging research domain, particularly in achieving accurate and real-time identification of facial expression. Existing approaches often struggle to manage dynamic variations in pose, illumination and expression, resulting in reduced accuracy, delayed processing and inconsistent identification. The research difficulties are addressed by introducing a facial recognition framework based on the unified optimised feature vector (UOFV) to enhance robustness and efficiency in constrained environments. The framework combines standard descriptors (e.g., LBP, HOG and HDG) with deep learning features (e.g., CNN-based embeddings), uniting local texture, directional intensity patterns and high-level semantic representations into a single discriminative feature space. These extracted features are then optimised using the binary grey wolf optimisation algorithm, selected for its strong balance between exploration and exploitation. The optimisation systematically selects the most relevant attributes while discarding redundancies, reducing dimensionality without compromising discriminative power and producing the optimised UOFV. The framework is implemented in MATLAB and validated on six public datasets: ORL, YALE, warpPIE10P, dbCMUfaces, LFW and CASIA-WebFace. For classification, six standard machine learning models (e.g., KNN, DT, SVM, NB, DA and RF) were used to identify facial images. Experimental results confirm the effectiveness of the UOFV approach, achieving 99.12% accuracy on ORL, 87.87% on YALE, 100% on warpPIE10P, 99.91% on dbCMUfaces, 98.56% on LFW and 98.48% on CASIA-WebFace. Moreover, the approach demonstrates remarkable efficiency, with an average execution time of only 1.2 milliseconds per recognition task, confirming its suitability for real-time applications.