# Fine-Tuning of Pre-Trained Deep Face Sketch Models Using Smart Switching Slime Mold Algorithm
## 1 Introduction  
Face sketch recognition involves matching two face images from different modalities; one is a face drawing created by a professional artist based on a witness statement, and the other is the corresponding image from the agency's department database. Some facial sketches are depicted for illustrative purposes, as given in Figure 1. Due to the partial and approximate nature of the eyewitness's description, face sketches are less detailed than their corresponding photos. Due to the modality disparities between face photos and sketch images, classic homogeneous face recognition algorithms perform badly in face sketch recognition [1]. To overcome this challenge, soft computing models have been utilized. Existing methods for face sketch recognition are resolved in three ways, which are synthesis-based, projection-based, and optimization-based. In the first category, synthesis-based methods, also known as generative methods, are used to transform one of the modalities into the other (photo to sketch or vice versa) prior to matching [2; 3; 4; 5; 6; 7; 8; 9; 10; 11]. Projection-based techniques utilize soft computing approaches [12; 13; 14; 15; 16; 17] to reduce the differences between sketch and photo features projected in a different space. The final category, which isoptimization-based, focuses on fine-tuning the parameters using optimization methods. In other words, the objective is to maximize the similarity between the sketch and its photo, maximizing the weights of their extracted features [18; 19; 20; 1]. Apart from that, deep learning approaches such as FaceNet [21], ArcFace [22], VGG-Face [23], and DeepFace [24] have recently achieved major advances in face recognition by learning latent embeddings from large amounts of face data. However, it is more difficult to use deep learning for face sketch recognition due to the lack of face sketch-photo data. This is due to the challenge of obtaining face sketch pictures where existing public datasets contain only a handful of face sketch-photo combinations, with one sketch per individual in most cases. Over-fitting and local minima arise from these limitations, making it hard for deep networks to learn adequate feature representations for face sketch recognition during training [25]. This study suggests adopting a recent optimization approach to solve this problem by performing fine-tuning of those pre-trained deep-face recognition models such as FaceNet, ArcFace, VGG-Face, and DeepFace. Recently, several optimizers have been presented in the literature, such as Fitness Dependent Optimizer (FDO) [26], Donkey and Smuggler Optimizer (DSO) [27], Slime Mold Algorithm (SMA) [28], Black Widow Optimization Algorithm (BWO) [29], The Red Fox Optimization Algorithm (RFO) [30], Q-Learning Embedded Sine Cosine Algorithm (QLESCA) [31], and Reptile Search Algorithm (RSA) [32]. Among them, SMA received a lot of attention due to its smooth structure, few parameters, robustness, and implementation flexibility. However, when dealing with complicated and high-dimensional situations, SMA still has some limitations, such as its slow convergence and potential to slip into the local optima trap [33; 34; 35]. Additionally, the optimization algorithm for slime molds utilizes the best leader and two randomly pooled slime molds from the population, resulting in poor exploitation and exploration [36; 37]. Thus, in order to fully optimize the performance of SMA and make it perfect for the face sketch recognition problem, we propose an enhanced variant named S\({}^{2}\)SMA. Accordingly, S\({}^{2}\)SMA will be applied to tackle the face sketch recognition problem. In summary, this work presents S\({}^{2}\)SMA with embedded rules and search operations, namely the Arithmetic Optimization Algorithm (AOA) and the Levy Flight (LF). Moreover, S\({}^{2}\)SMA has been applied to tune the weights of deep-face models to make it work for  
Figure 1: Sample of sketches (**a**) and corresponding photos (**b**). the problem of face sketch recognition.