Hello, everyone. I am Yafeng Yin.
Today, I will talk about our paper titled "TouchID: User Authentication on Mobile Devices via Inertial-Touch Gesture Analysis".
This is the outline of my presentation.
First, let's us talk about the motivation.
Nowadays, to protect the user's privacy on mobile devices, user authentication based on what the user knows, e.g., password or graphic pattern, was proposed.
However, they are easily suffering from attacks.
Recently, the biometric authentication based on what the user has, e.g., fingerprint or face, was proposed.
However, they often require dedicated hardware and suffer from replaying and spoofing attacks.
To overcome the previous limitations, the behavioral biometrics-based user authentication was proposed.
It works based on how the user does.
However, these customized gestures are rarely adopted on smartphones.
To provide a user authentication scheme which can be widely used in smartphone,
we propose TouchID, where the user performs a widely adopted graphic pattern based unlock gesture for authentication.
In TouchID, not only the graphic pattern but also the user behavior performing the gesture are used for authentication,
where the user behavior is monitored by built-in inertial sensor and touch screen.
Before we talk about TouchID, we first demonstrate some observations.
As shown in the red rectangle, the gestures of a same user have a good consistency.
While the gestures from different users have difference, as shown in the green rectangle.
It indicates that the built-in touch sensor and inertial sensor can be used to distinguish different users for authentication.
However, there are also existing several challenges.
The first challenge is how to mitigate the intra-user difference and reduce the inter-user similarity?
The second challenge is how to get a uniform representation for touch gestures with different topological structures.
The third challenge is how to tolerate the uncertainty caused by different body or hand postures.
This is the overview of TouchID, which includes three main components,
data collection and pre-processing, modeling of touch gestures, training and authentication.
The second one is the most important component, and we will talk about it in detail.
Considering the first challenge of intra-user difference and inter-user similarity,
we propose the spatial alignment and gesture segmentation schemes.
To reduce intra-user difference, we utilize the space consistency in gestures to align sensor data.
Specifically, we segment the gestures into multiple sub-gestures,
where each sub-gesture is a node or line, and align the sensor data through linear interpolation in each sub-gesture.
To reduce inter-user similarity, we segment the touch gesture into sub-gestures,
and then highlight the sub-gestures with low inter-user similarity for following feature selection,
as the sub-gesture shown in the middle figure.
Considering the second challenge from different topological structures of graphic patterns, we propose a uniform representation method.
Whatever the graphic pattern is, we classify the sub-gestures into four categories,
i.e., start node, end node, turning node (set) and smooth path (set).
Then, we select features based on Fisher Score in each part.
In this table, we show the final selected features for user authentication.
In each part, the feature set (i.e., types of features) is fixed, while the number of features can be different, since the number of sub-gestures in a part can be different.
Considering the third challenge caused by the uncertainty of body or hand postures, we propose multi-threshold KNN,
which adaptively generate different classification boundary for each posture, thus can authenticate users under different postures.
After describing the solution, now we talk about the evaluation.
This slide show the experiment setup, including the smartphone, volunteers, graphic patterns, performance metrics, and so on.
Overall, TouchID achieves a good performance in user authentication, the average equal error rate is only 4.9%.
In this slide, we show that each designed component in TouchID has contributed to a higher performance.
Besides, we can find that TouchID can work well under different scenarios.
In regard to the resilience to attacks, even under the mimicking attack, TouchID still has a good performance of user authentication.
In sum, we propose a user authentication scheme TouchID based on inertial-touch gesture analysis on mobile devices,
and design a series of schemes to make it work well.
That's all. Thank you very much!