311,050 research outputs found
A dynamic framework based on local Zernike Moment and motion history image for facial expression recognition
A dynamic descriptor facilitates robust recognition of facial expressions in video sequences. The current two main approaches to the recognition are basic emotion recognition and recognition based on facial action coding system (FACS) action units. In this paper we focus on basic emotion recognition and propose a spatio-temporal feature based on local Zernike moment in the spatial domain using motion change frequency. We also design a dynamic feature comprising motion history image and entropy. To recognise a facial expression, a weighting strategy based on the latter feature and sub-division of the image frame is applied to the former to enhance the dynamic information of facial expression, and followed by the application of the classical support vector machine. Experiments on the CK+ and MMI datasets using leave-one-out cross validation scheme demonstrate that the integrated framework achieves a better performance than using individual descriptor separately. Compared with six state-of-arts methods, the proposed framework demonstrates a superior performance
Spatiotemporal Augmentation on Selective Frequencies for Video Representation Learning
Recent self-supervised video representation learning methods focus on
maximizing the similarity between multiple augmented views from the same video
and largely rely on the quality of generated views. In this paper, we propose
frequency augmentation (FreqAug), a spatio-temporal data augmentation method in
the frequency domain for video representation learning. FreqAug stochastically
removes undesirable information from the video by filtering out specific
frequency components so that learned representation captures essential features
of the video for various downstream tasks. Specifically, FreqAug pushes the
model to focus more on dynamic features rather than static features in the
video via dropping spatial or temporal low-frequency components. In other
words, learning invariance between remaining frequency components results in
high-frequency enhanced representation with less static bias. To verify the
generality of the proposed method, we experiment with FreqAug on multiple
self-supervised learning frameworks along with standard augmentations.
Transferring the improved representation to five video action recognition and
two temporal action localization downstream tasks shows consistent improvements
over baselines
- …