Thank you for the introduction.
Hello everyone, this is ChaoFeng.
Now I gonna present our work, “RF-Identity: Non-Intrusive Person Identification Based
on Commodity RFID Devices”.
This work has been done with Northwest university and Umass.
Next, we will talk about our motivation.
Nowadays, person identification plays a critical role in a large range of applications.
For example, In a smart home, we can provide personalized services, such as switching TV
to the favorite channel, adjusting the room temperature and lighting conditions.
In mobile payment scenario, we can pay with a contact-free manner.
In entrance guard scenario, we can enter the room without a need for card touch.
Thus, person identification is very crucial for our daily life.
Next, we will talk about the current existing solutions for person identification.
Traditional person identification systems mainly use Password, Fingerprint, Iris recognition
or camera.
Although they can achieve promising performance, they have the following limitations, such
as memory load, replay attack, high cost and privacy leaks.
Fortunately, RF-based person identification methods provide a better way due to the appealing
contact-free nature.
Recently, some works have used RF signals to identify users.
These works can achieve a very high accuracy, but requiring users to keep stable walking
pattern, a camera or each user to carry an RFID card
Further, they cannot identify static users.
Hence, We present our RF-Identify, a system can both passively identify walking users
and static users.
RF-Identify has three advantages.
First, Low hardware cost, it works on cheap commodity RFID devices.
Second, high accuracy, it combines the unique walking pattern features and the body shape
features of a user.
Third, RF-Identity is easy to deploy.
Now that we know what we want to achieve, the question is how do we get there?
To present our basic idea, we conduct two benchmark experiments,
First, we let two volunteers walk naturally through the sensing area one by one.
In the second experiment, two volunteers are asked to stand still at the same position
for a period of 10 seconds.
For the walking case.
We can see that First, different tags have different amounts
of phase and amplitude changes.
Particularly, when the subject blocks the LoS path, the variations are large.
Second, different subjects cause different amounts of changes in phase and amplitude
readings.
For the static case, we observe that different users cause different
amounts of phase and amplitude at each tag.
Hence, these observations indicate that we can use the phase and amplitude readings to
identify the target.
Our key solution is integrating the walking pattern and the body shape features to identify
a dynamic user.
To extract the walking pattern, we design a two-path channel network to fuse phase and
amplitude readings to extract the walking pattern features.
In particular, we design an attention block to let the model focus more attention on those
tags whose readings are more sensitive to user’s walking.
To obtain body shape features, we first use amplitude difference information with RTI
technique to obtain constructed tomographic image for a walking target.
Then we utilize an encoder network to extract body shape features.
Finally, we combine the walking patterns and body shape feature to identify a user.
To demonstrate the combined features can work better than the walking features, we use tsne
tools to visualize the learned features, we can clearly see that the combined feature
is more distinguished.
Generally, there usually is a new user to register, traditional method is to retrain
the whole network, which is time-consuming.
To reduce the time overhead, we employ a transfer learning method to speed up the convergence
of the model parameters.
The basic idea is to freeze the weights of the feature extraction module and only fine-tune
parameters of updating module, thus substantially reducing the time and human labor cost for
data collection.
The last challenge is how to accurately identify a static user?
A key intuition is exploiting tag diversity in spatial domain to construct phase and amplitude
profiles as the biometric features.
As shown in the figures, we can see that different users exhibit different phase and amplitude
profiles constructed with data collected from spatially deployed tags.
However, when we let one subject stand still at a fixed position, and repeatedly collect
readings three times, we can see that the readings of some tags are stable while others
are not.
To overcome the above issue, we devise a weight-based distance method to differentiate profiles
of different users.
Where the first parameter represents the ability to identify different users, the second parameter
represents the similarity between the training and testing profiles.
Finally, we use the tsne tool to present the features, we can see different users have
different distributions.
Next, we deploy 12 tags to form a tag array on one side of the door, another side is the
reader.
The horizontal distance between the antenna and the middle point of the tag array is 1.5
m.
The height of the antenna is 1.2 m above the ground.
In our default experiments, we recruit 50 volunteers (28 males and 22 females).
Different subjects have different heights and weights.
We first present the overall person identification, We can see that our system can achieve an
average accuracy of 94.2% and 95.9% in dynamic and static person identification respectively,
which demonstrates RF-Identity works well in both cases.
Next, we explore the performance of feature fusion.
For the dynamic case, we can see that combining the walking pattern features and body shape
features can achieve the best performance.
In addition, we can see that combine the phase and amplitude information works better than
each of them.
We now evaluate the performance of transfer learning scheme.
We can see that it can reduce the training cost and retain a higher identification accuracy.
To explore the impact of different number of test users, we change the number of tested
users from 10 to 50.
We can see that the identification accuracy decreases with more users.
This result is expected because with more targets to be differentiated, the average
difference between targets’ features gets smaller.
To conclude, This work can achieve person identification
for both dynamic and static persons using commodity RFID devices.
We propose a set of schemes to identify dynamic and static users.
Finally, we conduct extensive experiments to demonstrate the effectiveness and robustness
of RF-Identity.
Thanks a lot for your listening.