# RFID Authentication System Based on User Biometric Information +
## 5 Extension of Authentication System
### System Design  
In this section, we will introduce the implementation process of the improved system, including five steps of data collection, data preprocessing, anomaly detection, feature extraction and authentication. Data collectionWhen registering, the user needs to touch each tag in a tag sequence specified by the system in order. For example, \"1379\" means that the user needs to touch the four tags 1, 3, 7, and 9 in sequence. The system can collect multiple sets of data when the user touches the tag with its own habits or rhythms. In the authentication phase, the user still only needs to touch the tags according to the tag sequence. Since the tag signal can change drastically with the touch, the user should make each touch last for a short period of time so that the system can obtain relatively stable tag phase values. After collecting the user data, the system needs to perform preprocessing and anomaly detection, and further extract the characteristic information of different users, and finally perform user authentication. Data preprocessingThe system needs to preprocess the raw data from the user to obtain continuous and stable tag signal. This part is consistent with the data preprocessing method used before the improvement scheme. Specifically, for each tag, the difference between its phase value and the average of the phases values of other tags around it should be calculated to distinguish whether the data are abnormal. Then, compare the value with the set threshold value to determine whether periodic signal surround or inverted \(\pi\) phenomenon occurs, and perform the corresponding data processing according to the determination result. Anomaly detectionIn this scheme, the label signal is detected by the sliding window. After obtaining the exception window using the previous anomaly detection algorithm, we can get the left exception window and the right exception window with fuzzy time characteristics. In the anomaly detection algorithm, if the size and moving step of the sliding window are both large, the time span of the left and right exception window is also large, so the exact time when the user touches the tag cannot be obtain. Therefore, it is necessary to further process the exception window to obtain more accurate time features. For the left exception window \(left\), the sum of its amplitude \(G(left)\) is larger than the threshold \(thres_{4}\), while \(G(left-1)\) is smaller than the threshold, indicating that the precise time of user touch must be between the left boundary \((left-1)\) of window and the right boundary \(left\) of the window. Take the beginning time of the window \((left-1)\) to the end time of the window \(left\) as the detection area of the exact time, and set the size and moving step of the sliding window to a smaller time granularity \(t\), and then the average amplitude of the tag phase [2] in the window can be recalculated as  
Figure 10: Authentication scheme based on users’ physiological and behavioral characteristics. The start and end time of each user touch on the tag can be obtained. \[A^{\prime}_{i}(k)=\frac{\sum\limits_{j=1}^{l}\left|\theta_{j}-\theta_{m}\right|}{l} \quad\text{and}\quad\theta^{\prime}_{m}=\frac{\sum\limits_{j=1}^{5}\theta_{j}}{5}, \tag{10}\]  
where \(l\) represents the number of tag phases in the window of size \(t\). The new amplitude function can be obtained [2] from the average amplitude of each tag in the window. \[G^{\prime}(k)=\sum\limits_{i=1}^{9}A^{\prime}_{i}(k). \tag{11}\]  
In anomaly detection, the time of the first window from left to right that detects an anomaly is taken as the precise time of the beginning of user's touch. Similarly, for the right exception window _right_, when the sum of its amplitude \(G(right)\) is greater than the threshold \(thres_{4}\), and \(G(right+1)\) is less than the threshold, it means that the precise time of user touch must be between the left boundary _right_ and the right boundary \((right+1)\) of the window.