# Secure and Privacy-Protected Bioinformation Implementation in Air Passenger Transport Based on DLT
## 3 Framework Architecture
### Facial Recognition Technology  
Facial recognition verifies or identifies an individual by analyzing and recognizing facial features. Firstly, face image data are captured through the camera. The captured data include various features of the face such as the position, size and relative relationship of the eyes, nose and mouth. In this research, a deep-learning-based facial recognition technique developed in recent years is used for this step and the stored face feature data are put into the DLT framework. This scheme uses the Convolutional Neural Networks (CNN) model to learn features from image data without manually programming the feature extractor. In the field of facial recognition, CNNs can learn complex patterns of the face through multi-level non-linear processing and effectively deal with changes in posture, lighting and expression. The following is the specific flow of the project implementation. I. Before training the CNN, a large number of facial images are first collected and preprocessed. The images are resized to a uniform-size square (224 \(\times\) 224 pixels) at the stage of image normalization. Next, the image colors are normalized in order to reduce the effect of lighting variations. Finally, the diversity of the training data are increased by rotating, scaling and cropping to improve the generalization ability of the model. II. In this study, CNN models are constructed based on the Inception architecture proposed by [PERSON] et al. in 2014 [26]. In facial recognition tasks, this architecture can recognize different facial features through its deep and complex feature extraction capabilities. Convolutional Layer: the entire image is scanned using multiple small convolutional kernels, these filters are capable of capturing local features of the image such as edges and corner points. By parallelizing multiple convolutional layers employing different convolutional kernels, the model captures information at different scales within the same layer. This design allows the network to learn multiple features in each module, enhancing the model's ability to perceive different parts of the image. The use of 1 \(\times\) 1 convolutional layers before larger convolutions effectively reduces the depth of the input to the larger convolutional kernel, thereby reducing computational complexity. It could meet the fast recognition speeds required for the use of facial recognition techniques in the airline service industry. Activation Layer: Use ReLU (Rectified Linear Unit) activation function to increase the non-linear capability of the network and help the network learn more complex patterns. Pooling Layer: Reduces the spatial size of the feature map, reducing the number of parameters and the amount of computation while maintaining important information. Fully Connected Layer: At the end of the network, the learned features are transformed into a final output, such as a vector, which represents the high-level features of the face. III. Finally, the training of the model is completed. This process includes forward propagation, loss calculation, backpropagation, and parameter updating. Figure 2: Interactions of nodes based on CorDapp. The trained deep-learning model extracts a high-dimensional feature vector from each facial image. This vector encodes key information about the face, such as facial structure, key point locations, and texture. Next, the facial features are to be stored in a database in the form of hash values for fast retrieval and comparison. Based on the task scenario of this research and the practical needs of airline service industry applications, this process requires fast response with low computational cost to shorten the service time and improve passenger satisfaction. Therefore, this scheme adopts LSH (location-sensitive hashing) in this step [27]. It performs hash operations and similarity searches quickly and is well suited for real-time queries. Also, the LSH algorithm is relatively simple, easy to implement and maintain, and does not require a complex training process.