Support Vector Machines Optimisation for Face Recognition Using Sparrow Search Algorithm

In the realm of face recognition utilising Support Vector Machines (SVM), the adaptivity of the penalty parameter c and the kernel function g is often found lacking, leading to suboptimal recognition rates. To address this issue, an approach harnessing the Sparrow Search Algorithm (SSA) for SVM parameter optimisation has been proposed. Traditional methods such as grid and random search, alongside other swarm intelligence optimisation algorithms like Particle Swarm Algorithm (PSO) and Differential Evolutionary Algorithm (DE), were surpassed by the capabilities of the SSA in numerous applications. Cross-validation (CV) was employed, with the SVM model training recognition accuracy serving as the SSA fitness value. Upon achieving optimal fitness values, the best combination of hyperparameters was ascertained. The overarching aim was to deploy the SSA for global optimisation of SVM's penalty parameters and kernel function, ensuring the derivation of the globally optimal solution for the ultimate classifier model in face recognition tasks. An empirical analysis conducted on the ORL standard face database revealed that the proposed method outperformed the PSO, DE, Gray Wolf Algorithm (GWO), and Enhanced Gray Wolf Algorithm (EGWO), registering an average accuracy of 95.1%. This starkly contrasts with the 81.9% accuracy of the traditional SVM. Such results demonstrate the method's efficacy in enhancing recognition performance, offering a novel avenue to elevate the accuracy of conventional SVM-based face recognition.