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In this work, we bring to light a novel face recognition (FR) system based on modified shuffled frog leaping algorithm (MSFLA) blended to Gabor wavelets. This new approach operates straightly on feature extraction and selection stages by providing the most propitious Gabor representations of a face image. While many researchers are seeking to find better parameterisation for Gabor filters, we introduce our evolutionary MSFLA-Gabor prototype combined to support vector machine (SVM) classifier as a robust contribution in the face biometric field. Primarily, we start by highlighting the impressive quality insured by Gabor filters in salient point extraction. Next, we present the potential dynamism of metaheuristic MSFLA in enhancing feature selection as well as up grading SVM classifier performance. Then, our optimised MSFLA-Gabor-SVM algorithm is tested on three databases under varied facial expressions, illuminations and poses. The experimental results have shown higher recognition rates and lower computational complexity scores than previous techniques.