Hello everyone, I am Atsushi Nakazawa from Kyoto University.
We will present the method of GAN-based style transformation to improve gesture recognition
accuracy.
Gesture recognition using weable sensing devices is more important in resent smartphones, VR
or MR devices, as well as novel applications such as medical or nursing fields.
Here, fine-grained gesture recognition with high accuracy is highly demanded.
But there is a fundamental problem: which is the individual differences of sensory data.
This example shows the gesture data performed by different users.
As seen, the signals are quite different even in the same gesture classes.
It frequently happens that the gesture data of one class may similar to the gesture of
another class performed by different individuals.
So, individual differences of gesture data is a fundamental problem in gesture recognition.
To solve this problem, personalized gesture recognition that use personal gesture sample
is the effective solution, but it requires a lot of data collection cost and lose usability.
Thus our idea is generating missing data by using GAN-based style translation because
there are individual styles and gesture-class styles.
This reduces the data collection cost and increase the gesture recognition accuracy
of the target individuals.
This is the algorithm, given input gesture data, source and target style labels, generate
generates fake gesture data
Then, the descriminator discriminates the real/fake of the input motion as well as style
classes.
At the same time, the generater transform the generated data to the original domain
and obtain the reconstructed data.
We used five loss function, adversarial loss, style class loss, content class loss, cycle-consistency
loss and smoothness loss.
In evaluation, we used different types of sensor data.
One is the cheekinput dataset and USC-HAD dataset.
This shows the style translation examples
This shows the accuracy evaluation of the proposed and existing algorithm increasing
the number of missing gesture classes.
The real lines shows the proposed algorithm and dashed lines shows the existing algorithm
that does not use generative data.
As seen the proposed algorithm does not descreases as the missing data incrases while the existing
algorithm decrease the lot.
In the end, our method outperformed existing algorithm only the use of 25% of the target
data in CheekInput and that of 43% in USC-HAD.
This show the power of the proposed generative approach.
Our goal is achiving gesture recognition of many classes in high accuracy.
We introduced two solutions.
The first solution is introducing the personalized recognition.
Using this, our accuracy increases to 74%, the second solution is GAN-based data synthesis.
By using this, the accuracy increased up to 84%.
This is a big margin to the existing algorithms.
Thank you for your attention.