Face sketch recognition using a hybrid optimization model

In this work, a hybrid optimization-based model is introduced to handle the problem of face sketch recognition. The proposed model comprises a total of three layers that are global search layer, control layer, and fine-tuning layer. The global layer contains a set of search operations from particle swarm optimization (PSO) algorithm to perform the task of global search. However, the control layer is responsible about controlling the execution of the implemented search operations at run time. Finally, the fine-tuning layer is aimed at performing search refinement to enhance the search ability. For sketch recognition, the proposed hybrid model is applied on the input face sketch to locate the internal sketch facial components. Three types of texture features extraction techniques are adopted in this study including Histogram Of Gradient (HOG), Local Binary Pattern (LBP), and Gabor wavelet. To assess the performances of the proposed model, a total of three face sketch databases have been used which are LFW, AR, and CUHK. The reported results indicate that the proposed hybrid model was able to achieve a competitive performance with 96% on AR, 87.68% on CUHK, and 50.00% on LFW. Additionally, the outcomes reveal that the proposed model statistically outperforms others PSO-based models as well as the state-of-the-art meta-heuristic optimization models.