Abstract:
This paper presents an approach for hand based
micro-gesture recognition in images and videos as part of the
Holoscopic Micro-Gesture Recognition (HoMGR) challenge.
The database consists of Holoscopic 3D Micro-Gesture images
and videos. The proposed framework is an ensemble of convolutional
neural network and deep neural network. The framework
performs feature fusion technique on both handcrafted
(local phase quantization) and deep features extracted from
the neural network, to leverage on complimentary information.
The powerful discriminative nature of the fused features has
proved beneficial on the given HoMGR challenge data. The
experiments show that the proposed approach is effective and
outperforms the baseline on the Test set by an absolute margin
of 26.67% for images and 2.47% for videos, respectively
