I am Huijie Chen from Beijing Institute of Technology, and I will present our work, Listen
to Your Fingers: User Authentication Based on Geometry Biometrics of Touch Gesture.
People always use smartphone store personal information, online payment.
To prevent the smartphone to be accessed by strangers, many user authentication method
are provided. Such as facial recognition, fingerprint, and passcode.
In contrast, the passcode is convenient, low hardware cost, and very comfortable. And more
user like it.
However, passcode become less reliable. Because it is high possible to be inferred by many
side-channel attacks.
Of couse, many effort has been made to provide a additional verification for passcode.
For example, the behavior biometrics and geometry constrains between fingers are used for authentication.
However, they always less accurate or has limited scenario.
These limitations motivate us to design a reliable second factor user verification for
passcode.
We observe that when input a passcode. The user hand posture is diverse. And the hand
geometry has individual difference.
Besides, the hand posture spatial feature can be extracted from the acoustic multipath
signal.
We will use acoustic sensing to scan the user’s hand posture and extract the spatial pattern
for authentication.
Three challenges needs to be addressed.
how to get the acoustic fragment when the finger stop at some Landmark positions.
how to extract Fine-grained multipath features?
And how to design a Secure authentication method with limited samples?
The system overview comprises of three components.
Acoustic signal segmentation, hand geometry biometrics profiling, and authentication model.
To get the acoustic fragment, we estimate the finger static period with the finger trace
data.
And determine the start time in the acoustic signal based on the tapping sound event.
The sampling frequency of the finger traces is not consistent.
Therefore, we first use linear interpolation method.
And then calculate the slope and detect these red point.
For the acoustic signal, we use a band pass filer to remove noise.
Then extract the envelope, and search backward from the first peak to locate the tapping
sound.
We relies on the auto-correlation characteristic of the Zadoff-Chu sequence to Extract the
fine-grained multipath feature.
This figure is a example of ZC sequence.
it needs to be modulated into a acoustic signal for active acoustic sensing.
First, we increase the sampling rate in the frequency domain.
Then, make it inaudible for human by introduce a carrier signal Fc.
To get the multipath response, circle cross-correlation is used for each acoustic fragment.
Only the part near the direct path response will be used for feature extraction.
The authentication model comprises of two stages:register stage and verification stage.
In the first stage, the personal feature is generated. we align these samples and generate
the occupancy map.
And in the verification stage, once the user input a sample,
the similarity will be calculated for user authentication.
30 volunteers participate in our experiment.
and evaluation is performed in three types of environment.
First we study the overall performance of our system.
The average authentication accuracy is higher than 90 percent.
we evaluate the reliability of our system against the replay attack
and the Observe and Imitate Attack.
The result show that our approach can effectively resist these attacks.
Our work target to Provide a second factor verification for passcode based authentication
method.
It rely on the biometrics related with the touching gesture 3D shape. And robust to the
touch behavioral variability.
We use active acoustic sensing to extract the unique spatial pattern from the multipath
signals for user authentication.
The challenges contains Acoustic signal segmentation, Touch posture biometrics profiling, Authentication
model
The evaluation shows it can effectively resist many attacks (e.g., replay attack and observe-imitate
attack).
That's all. Thanks for listening.