# Fine-Tuning of Pre-Trained Deep Face Sketch Models Using Smart Switching Slime Mold Algorithm
## 2 Related Work  
As previously mentioned, face sketch recognition methods can be categorized into synthesis-based, projection-based, and optimization-based. This section highlights the studies that have followed the optimization methods. Many optimization approaches are used in the literature for face recognition, e.g., PSO-based [39; 40; 41; 42; 43], Firefly Optimization Algorithm-based [44], Harris Hawks Optimization-based [45], and Bald Eagle Search Optimization-based [46], which have been effectively applied to the problem of face recognition. However, only these studies focused on optimization-based methods for handling the problem of face sketch recognition. For example, the earliest study mentioned in this literature review is by [PERSON] et al. [18], who proposed a weight optimization technique based on the Genetic Algorithm (GA) to determine the optimal weights for every facial patch. Their model was evaluated on sketch-digital picture pairs from the CUHK and IIIT-D databases. The results indicated that their approach could offer superior identification performance compared to existing algorithms. Later, [PERSON] et al. (2012) combined the Genetic Algorithm (GA) and Simulated Annealing (SA) to address the issue of face sketch recognition [19]. GA-SA adjusted the weights of extracted features via a Local Binary Pattern (LBP). The experiments showed that the GA-SA method was better than other face recognition algorithms and two commercial systems. Moving to recent studies, [PERSON] et al. (2019) introduced a hybrid optimization model that combined PSO and a local search strategy to perform localization and weighting of the facial sketch region [1]. They employed three distinct extraction strategies for texture features: Histogram of Gradient (HOG), Local Binary Pattern (LBP), and Gabor wavelet. Several datasets were used to assess their proposed hybrid model, such as LFW, AR, and CUHK. The results demonstrated that the hybrid model achieved 96% on AR, 87.68% on CUHK, and 50% on LFW. Statistically, the suggested approach outperforms PSO-based models and state-of-the-art optimization methods. In the most recent study, [PERSON] et al. (2022) introduced a hybrid deep learning model for face sketch recognition that combined Particle Swarm Optimization (PSO) with the VGG-face deep learning network [20]. Their work incorporated PSO to determine the necessary VGG-facial filters and fine-tuned the weights of these selected filters for face sketch recognition. The outcomes showed PSO's ability to improve model accuracy and reduce complexity. Recent studies explored non-optimization-based methods for handling face sketch recognition. [PERSON] et al. [47] presented an unsupervised CycleGAN-based model that converted face sketches into high-quality photos by incorporating a multi-scale feature extraction module and a CBAM module to improve feature extraction and reduce background interference. [PERSON] et al. [48] proposed a GAN-based method for generating color face photos from hand-drawn sketches with improved accuracy and reduced artifacts. The proposed method by [PERSON] et al. [49], called Unsupervised Self-Attention Lightweight photo-to-sketch synthesis with Feature Maps (USAL), added a self-attention module to a generative adversarial network and reduced the layers of the discriminator to improve efficiency and performance. Furthermore, [PERSON] et al. [50] proposed an Intra-Domain Enhancement (IDE) method for face photo-sketch synthesis. The MLA-GAN model was designed to refine and extract features and enhance the resolution of the synthesized image, which outperformed the state-of-the-art face sketch synthesis methods on multiple public face sketch databases. In addition, [PERSON] et al. [51] introduced a Sketch-Guided Latent Diffusion Model (SGLDM) that used a Multi-Auto-Encoder (AE) to encode input sketches for denoising. The paper also proposed Stochastic Region Abstraction (SRA) as a data augmentation strategy to improve the model's robustness in managing sketches with varying levels of abstraction.