## 1 Introduction
The intensity-based Mueller algebra is promising as the \(4\times 4\) Mueller matrix completely characterizes the polarimetric properties of samples, including linear/circular diattenuation, linear/circular retardance, linear/circular polarizance, linear/circular depolarization, and so on [35; 48; 49; 50]. Direct physical meanings of the 16 Mueller matrix elements individually are usually ambiguous, thus a decomposition of the Mueller matrix is required [35; 51; 52]. The main decomposition method is the Mueller matrix polar decomposition (MMPD) proposed by [PERSON] and [PERSON] [53; 54; 55; 56]. The Mueller matrix imaging polarimeter (MMIP) technology with the MMPD method has been widely used for characterizing tissular-level (nerve bundle [37], vessel [57], muscle [58], etc.) and cellular-level (nucleus [32], chromosome [59], etc.) microstructures in different tissues. During the past decade, several attempts have been made for differentiating MM from BE by detecting retardance using an MMIP [31; 60]. However, the experiments are carried out on small animals. Although the similarity between animal tumor models and human tumors is high, the gap between the two is still undeniable. Therefore, the direct use of human skin models would help to better understand the development of human skin cancer. Furthermore, most MMIPs still struggle to perform high-fidelity Mueller matrix imaging at higher resolutions due to the lack of a proper calibration strategy [58; 61; 62; 63]. Recently, our group has developed a high-fidelity MMIP in combination with a rational calibration method, and the excellent diagnosis ability has been approved in the tendon [46], lung [64], and skin [65] tissue lesions. The reliability of this MMIP has also been validated, demon starting its remarkable clinical potential [65]. Thus, the homemade high-fidelity MMIP can provide accurate information on anisotropic structures by detecting the retardance of polarized light. In this paper, we prepare a set of human skin tissue sections, including normal, BE, and MM. Considering the experimental convenience and time costs, the sections are dewaxed, unstained, and unsealed. By employing the homemade high-fidelity MMIP, we detect the Mueller matrix from different unstained skin sections. Combined with the MMPD method, the retardance-related parameters are compared and analyzed between different diseases using the support vector machine (SVM) algorithms. In addition, we implement a true-color vectorial retardance imaging technique to differentiate MM from BE. The true-color imaging method based on encoding stokes parameters has been used to visualize birefringent tissues (e.g., muscles, nerve bundle, and vascular walls) [47]. By encoding multiple polarization-derived parameters (e.g., retardance, depolarization, diattenuation), this method has also been used to extract sample components (e.g., collagen and elastic fibers) [34; 66]. In this paper, we implemented the true-color imaging technique based on one parameter, namely the vectorial retardance to enhance the contrast of birefringent microstructures for classifying the three types of skin tissues. The true-color vectorial retardance, encoding the three retardance components into color space, contains not only the magnitude but also the fast axis information of retardance. Thus, the technique allows simultaneous observation of the distribution and orientation information of the anisotropic microfibrillar structures, such as collagen fibers. This imaging technology will assist doctors as well as dermatologists in making a quick and readable assessment of skin pathologies.