Abstract
Human identity recognition has a wide range of application scenarios and a large number of application requirements. In recent years, the technology of collecting human biometrics through sensors for identification has become mature, but this kind of method needs additional equipment as assistance, which cannot be well applied to some scenarios. Using Wi-Fi for identity recognition has many advantages, such as no additional equipment as assistance, not affected by temperature, humidity, weather, light, and so on, so it has become a hot topic of research. The methods of individual identity recognition have been more mature; for example, gait information can be extracted as features. However, it is difficult to identify small-scale (2–5) group personnel at one time, and the tasks of fingerprint storage and classification are complex. In order to solve this problem, this paper proposed a method of using the random forest as a fingerprint database classifier. The method is divided into two stages: the offline stage trains the random forest classifier through the collected training data set. In the online phase, the real-time data collected are input into the classifier to get the results. When extracting channel state information (CSI) features, multiple people are regarded as a whole to reduce the difficulty of feature selection. The use of random forest classifier in classification can give full play to the advantages of random forest, which can deal with a large number of multi-dimensional data and is easy to generalize. Experiments showed that WiGId has good recognition performance in both LOS (line of sight) and N LOS (None line of sight) environments.
Highlights
With the increasing popularity of wireless devices, more and more people begin to study the use of wireless devices for indoor location, human behavior perception, and human identification.Traditional identification methods are mainly realized by some auxiliary devices, such as wearingID cards to represent identity, biometric identifiers, such as fingerprints, iris, and so on
One of the advantages of random forest is that it can deal with a large number of high-dimensional data; this paper presented a group identification method based on random forest fingerprint database
In Algorithm 1, the original data is first processed by principal component analysis (PCA) and low-pass filter, and the processed data is used as the training data set of the decision tree
Summary
With the increasing popularity of wireless devices, more and more people begin to study the use of wireless devices for indoor location, human behavior perception, and human identification. Reference [8] proposed a personal identification method based on gesture features, which uniquely identifies users by establishing the relationship between different user gesture features and CSI It has high accuracy in the environment where the multipath effect is simple, but it can hardly play a role in the environment where the multipath effect is complex. Reference [9] proposed a method that collects gait information of users and classifies and identifies them through neural networks This method uses 23-layer deep convolution neural networks and can achieve an accuracy of about 90% from a group of 24 people at most. Reference [15] used the method of deep learning to identify the movements of the human body and distinguish different people, using the idea of collecting a large number of data, and putting the data into a model with strong computing power for training.
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