The working principle of a face recognition access control system typically encompasses the following steps:

1. Face Data Acquisition: The system utilizes devices such as cameras or infrared cameras to capture face images or video data. During acquisition, individuals are usually required to stand at a designated position and prepare themselves to ensure high-quality image capture and clear facial visibility.

2. Preprocessing and Feature Extraction:

   • Preprocessing: The captured face images undergo preprocessing, which might involve image denoising (to remove noise points from the image), grayscaling (converting color images to grayscale), or normalization (adjusting image pixel values to a specific range). These steps enhance the accuracy and efficiency of subsequent processing.

   • Feature Extraction: Computer vision and pattern recognition algorithms, such as Principal Component Analysis (PCA) or Local Binary Patterns (LBP), are employed to extract key features from the face images. These features, including facial contours, shapes and positions of facial features, and textures, represent the uniqueness of each face.

3. Feature Matching and Comparison: The extracted face features are matched and compared with those stored in the system's database. The database contains the facial feature information of pre-registered authorized users. Comparison algorithms, such as Euclidean distance (calculating the distance between feature vectors) or Cosine Similarity (measuring the cosine of the angle between feature vectors), are utilized to calculate the similarity or distance between the face to be recognized and each face in the database.

4. Decision-making and Recognition: Based on the comparison results, a decision is made. If the similarity between the face to be recognized and a face in the database reaches a predefined threshold (e.g., above 80% similarity), the system deems the face recognition successful, identifying the individual as a registered user. Otherwise, the system rejects the recognition request, classifying the individual as unauthorized.

5. Access Control: Upon successful recognition, the access control system performs the corresponding operation, such as unlocking an electronic door lock or granting access. Simultaneously, it may record relevant passage information, including time, date, and individual identity. If recognition fails, the door remains locked, prohibiting entry, and may trigger an alarm or prompt.

Additionally, to enhance security and prevent fraud, some face recognition access control systems incorporate other technologies, such as liveness detection (utilizing infrared or depth cameras), to prevent deception by fake faces (e.g., photos, masks, videos). As AI and deep learning evolve, face recognition algorithms continue to improve and optimize, enhancing the system's accuracy, speed, and adaptability to varying lighting conditions, face angles, and expressions.