# Personal Identification Using Embedded Raspberry Pi-Based Face Recognition Systems
## 3 Verification System  
### Selecting the Similarity Threshold  
The similarity threshold is a value that represents the degree of biometric similarity between the tested face and the reference face. The system returns a value between 0 and 1, where a higher value indicates a greater likelihood of a correct match. The appropriate threshold must be carefully selected based on factors such as image resolution, system performance, and the specific application requirements. Any value above the set threshold is considered a match, while values below the threshold are classified as mismatches. Adjusting the threshold allows for flexibility in the system's sensitivity, balancing accuracy and performance. For applications like identifying visitors in amusement parks for souvenir photography, a lower threshold may be acceptable, as only a single correct match is required, and the risk of false positives is not critical. However, in high-security systems such as access control or banking transactions, accuracy is paramount, and a higher threshold is necessary, even if it leads to longer identification times. Selecting the appropriate similarity threshold presents a challenge because setting a value of 100% (a threshold of 1) would require the tested image to be identical to the reference image, leading to impractically long identification times and a vast number of test images. This is due to various factors affecting recognition, such as facial position, equipment quality, system performance, lighting conditions, and even minor facial changes (e.g., a day-old beard). Conversely, setting an excessively low threshold could result in the system misidentifying users by recognizing them as the reference person they resemble most biometrically, even if they are not an exact match. To determine the optimal threshold, several tests were conducted using a group of six male individuals with varying degrees of biometric similarity. In selecting the sample group for this study, key biometric variations known to influence facial recognition accuracy were considered. These variations included demographic diversity, encompassing age groups such as children, young adults, middle-aged adults, and seniors. Gender representation was balanced to ensure inclusivity. Ethnic backgrounds were diverse, incorporating participants from various ethnic groups to assess algorithmic bias. Physical features, such as facial hair (e.g., beards, mustaches), and accessories like glasses, hats, or scarves, were also included. Skin tone variations and different facial expressions, including neutral, smiling, frowning, and other expressions, were tested. Additionally, unique features like scars, tattoos, or asymmetrical facial features were considered. Twelve participants took part in the study, including eight males of different ages (from 16 to 49 years old) and four females of different ages (from 19 to 51 years old). All users were entered into the database. In the following part of the article, only selected images (photos) of volunteers are presented. These tests helped identify a suitable threshold value for achieving the desired balance between accuracy and efficiency (Figure 11). In the first test, only the first user ([PERSON]) was introduced into the system. Then, each subject underwent the identification process at different similarity threshold values. The results are presented in Table 1.