4,592 research outputs found
EmoNets: Multimodal deep learning approaches for emotion recognition in video
The task of the emotion recognition in the wild (EmotiW) Challenge is to
assign one of seven emotions to short video clips extracted from Hollywood
style movies. The videos depict acted-out emotions under realistic conditions
with a large degree of variation in attributes such as pose and illumination,
making it worthwhile to explore approaches which consider combinations of
features from multiple modalities for label assignment. In this paper we
present our approach to learning several specialist models using deep learning
techniques, each focusing on one modality. Among these are a convolutional
neural network, focusing on capturing visual information in detected faces, a
deep belief net focusing on the representation of the audio stream, a K-Means
based "bag-of-mouths" model, which extracts visual features around the mouth
region and a relational autoencoder, which addresses spatio-temporal aspects of
videos. We explore multiple methods for the combination of cues from these
modalities into one common classifier. This achieves a considerably greater
accuracy than predictions from our strongest single-modality classifier. Our
method was the winning submission in the 2013 EmotiW challenge and achieved a
test set accuracy of 47.67% on the 2014 dataset
Spontaneous Subtle Expression Detection and Recognition based on Facial Strain
Optical strain is an extension of optical flow that is capable of quantifying
subtle changes on faces and representing the minute facial motion intensities
at the pixel level. This is computationally essential for the relatively new
field of spontaneous micro-expression, where subtle expressions can be
technically challenging to pinpoint. In this paper, we present a novel method
for detecting and recognizing micro-expressions by utilizing facial optical
strain magnitudes to construct optical strain features and optical strain
weighted features. The two sets of features are then concatenated to form the
resultant feature histogram. Experiments were performed on the CASME II and
SMIC databases. We demonstrate on both databases, the usefulness of optical
strain information and more importantly, that our best approaches are able to
outperform the original baseline results for both detection and recognition
tasks. A comparison of the proposed method with other existing spatio-temporal
feature extraction approaches is also presented.Comment: 21 pages (including references), single column format, accepted to
Signal Processing: Image Communication journa
Multimodal person recognition for human-vehicle interaction
Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies
- …