Hello, everyone, I'm Hangcheng Cao from Hunan University.
It is my honor to report our work on Ubicomp twenty and twenty-one.
The title of my report is "SmileAuth: Using Dental Edge Biometrics for User Authentication on Smartphones".
In fact, this work provides a new authentication method for camera-enabled smart devices.
It was done by Hongbo Jiang, Daibo Liu, Jie Xiong, Zhichao Cao, and me.
I would like to thank them and all volunteers for their efforts in this work.
As we know, smartphone plays a critical role in human daily lives, responsible for information interaction, device control, finance management, etc.
For malicious users, smartphones are of great intrusion and attack value.
Fortunately, identity authentication is an effective way to manage access permission and hence can protect private information.
Currently, most smartphones supporting PINs and biometrics-based authentication ways, to verify user identity.
However, they all have their own shortcomings . For example, PINs are easily stolen by someone else;
Reliable face authentication systems always require non-trivial sensors.
Therefore, a new authentication way is desired that can further balance the relationship of security, convenience, and cost.
In this paper, we propose a novel user authentication method, called SmileAuth, that leverages unique features of dental edge biometrics.
SmileAuth simply requires the user to open his/her mouth with exposing teeth while slightly moving the smartphone;
then it captures dental edge lines from continuous images.
The reasons for dental structure uniqueness among users come from two aspects:
differences in size, shape, and arrangement mode of teeth;
acquired eating habits among users.
In a feasibility study, we show dental edge lines from five users and they are distinguishable.
In addition, dental edge lines of the same user captured at multiple camera angles are different.
Therefore, before the feature similarity comparison, we have to judge the camera angle of images.
In SmileAuth, we propose a series of solutions to construct the model, which can be divided into four parts.
They are image preprocessing, dental feature extraction, feature matching, and authentication decision making.
The specific technical details can be found in our paper, so I won’t repeat them here.
After obtaining an image, we obtain dental edge lines through three steps.
They are dental area segmentation, edge detection by Sobel operator, and noise point removal.
Next, we extract four types of features from original edge lines to represent user identity.
In addition, we calculate the weight of each feature through the random forest to explore their impacts on model construction.
Next, we leverage the characteristics of the mouth landmarks to estimate the viewing angle of the camera with respect to the mouth.
The key intuition here is that when images are captured from different camera angles, the distance between the same landmark pair varies accordingly.
The Euclidean distance thus has a strong correlation with the camera angle.
The smaller Lambda of two images means that their camera angles are more similar.
We collect data from 97 volunteers in three different environments.
The specific details are not repeated here.
In the experiment, we evaluate the overall system performance of SmileAuth.
Thanks to normalization operation on dental features, the impact of distance between the smartphone and mouth on authentication is negligible.
This result shows that the ratio of legal users denied by SmileAuth increases in a low light environment but has little impact on user experience.
An FNR of 5.27% in mobile scenario doesn’t affect the usability much because the legal user can pass the authentication with multiple tries.
In general, we proposed a new user authentication method SmileAuth in this paper by using dental edge biometrics.
We demonstrated that dental edge lines are unique for each individual and thus be used to achieve highly accurate user authentication.
We designed a series of solutions to extract dental edge lines from originally captured images and resist potential attacks.
SmileAuth can be regarded as a supplementary component of existing authentication schemes, to further enhance private security.
Thank you for your attention. Please contact me if you have any questions.