Hello, everyone. I'm Yaqian Wei from Beijing Institute of Technology, China.
I'm honored to present the paper IriTrack: Face Presentation Attack Detection Using
Iris Tracking. This is a joint work with my teachers and teammate at BIT.
Nowadays, Face Recognition systems have become popular as a reliable and convenient way of
user identification, access control and online banking and payment. A Face Recognition system
is composed of Presentation Attack Detection and Face Recognition. The former ensures that
the image or video samples are provided by a genuine user, while the latter has to determine
whether the user is authorized. In this paper, we focus on developing efficient PAD methods.
In presentation attacks, an adversary attempts to subvert a recognition system by presenting
an artefact that mimics facial biometric features. In order to detect artefacts,
PAD methods are proposed. PAD methods can be classified into two categories. Methods using
static features are computationally inexpensive and responsive, but error-prone and unstable.
Methods using dynamic features can better defend against artefacts, but
increase storage and computational overhead. In this paper, we pay attention to the PAD methods
relying on eye motion detection or eye-based challenge response detection. Czajka proposed
a method based on human pupil dynamics, i.e., irises would change their sizes in different
light intensity levels. However, pupil size can be altered in different psychological states,
leading to degradation of detection accuracy. Ali et al. proposed a method based on an assumption:
the horizontal or vertical movement of real eyes is more stable than that of unreal eyes. However,
the gaze of a real person changes unconsciously, which results in an offset of gaze positions.
The goal of this paper is to achieve an efficient and robust PAD method via tracking the position of
human irises. There are mainly two challenges. On the one hand, how can we decrease the influence of
noises and different adjustment strategies. On the other hand, we should make sure
the method efficient and robust. The main contributions are as follows.
First, we propose IriTrack, a PAD method that leverages poly-line-based iris-tracking with
computational simplicity and no special hardware. Second, we design two key components of IriTrack,
namely a probability-based PAD pattern generation method and a precise iris tracking method. Third,
we conduct extensive experiments to demonstrate the effectiveness and efficiency. The idea is
inspired by the screen lock pattern systems, where lines are drawn by fingertip over dots
and then compared with the pattern pre-defined. In IriTrack, a user is required to move eyes
along a pattern, where the iris trajectories recorded are compared with the pattern to
draw a conclusion. The workflow is described as follows. First, the system randomly generates and
displays a PAD pattern on the screen. Then, the user is required to follow the pattern with eyes
and the trajectories of irises can be recorded by a camera. Finally, the collected trajectories and
the given pattern are fed to the similarity measurement module to draw a conclusion.
To achieve a balance between time efficiency, security, and accuracy, we design a probability-based
model of pattern generation. Specifically, we generate a pattern with only one line
segment, and then judge whether a new dot should be added. If the result is positive, we select an
angle and a line to update the pattern. Finally, we compute the goodness of the generated pattern
to ensure the time efficiency and security. To track iris movements, the tracking module utilizes
the camera to acquire images at a fixed frequency. Then, it employs the Daugman’s
integro-differential operator to detect the center of irises. In terms of similarity measurement,
to eliminate the influence of unconscious eye movements, we view the angles as the skeleton
of a pattern. Specifically, we compute iris positions first
and recover the angles. Then we measure the similarity between the skeleton and the pattern.
The system was deployed on a commercial PC and experiments were conducted with a default setting.
We select three comparison methods and use APCER and BPCER as accuracy metrics.
First, we investigate how time cost and accuracy vary according to different values of parameters.
In the experiment, the generated patterns are simplified into a poly-line consisting of only
3 dots. For one pattern, 4 participants are involved and each completes 10 times.
The results show that shorter lines with a higher speed are preferred to improve time efficiency.
To balance time efficiency and accuracy, we select the values of speed and line
segments. Then, we evaluate the performance of IriTrack and other PAD methods. The results
show that IriTrack owns the highest detection accuracy with moderate time and memory costs,
which shows that IriTrack has the best performance. We also evaluate the effect
of environmental and positional factors on the performance. For the factor of light intensity,
we find that the detection accuracy maintains at a high level as the light intensity changes.
For the factor of distance, we find that IriTrack achieves stable detection accuracy.
And for the factor of head pose, we find that an irregular facial pose can identify more artefacts.
Finally, we select 5 different combinations of the 3 factors.
The results show that combining these factors will not lead to additive effects on the performance.
And the accuracy is determined by a single dominating factor. The experimental results show
that IriTrack is robust to changes of lighting conditions, face-camera distances, and head poses.
Now I’d like to conclude this paper. We propose an efficient and robust PAD method suitable for
deployment on resource-constrained devices via tracking the position of human irises,
and demonstrate its effectiveness and efficiency. In the future,
we will further investigate more practical influencing factors to improve the performance.