111,749 research outputs found
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
Towards Automatic Speech Identification from Vocal Tract Shape Dynamics in Real-time MRI
Vocal tract configurations play a vital role in generating distinguishable
speech sounds, by modulating the airflow and creating different resonant
cavities in speech production. They contain abundant information that can be
utilized to better understand the underlying speech production mechanism. As a
step towards automatic mapping of vocal tract shape geometry to acoustics, this
paper employs effective video action recognition techniques, like Long-term
Recurrent Convolutional Networks (LRCN) models, to identify different
vowel-consonant-vowel (VCV) sequences from dynamic shaping of the vocal tract.
Such a model typically combines a CNN based deep hierarchical visual feature
extractor with Recurrent Networks, that ideally makes the network
spatio-temporally deep enough to learn the sequential dynamics of a short video
clip for video classification tasks. We use a database consisting of 2D
real-time MRI of vocal tract shaping during VCV utterances by 17 speakers. The
comparative performances of this class of algorithms under various parameter
settings and for various classification tasks are discussed. Interestingly, the
results show a marked difference in the model performance in the context of
speech classification with respect to generic sequence or video classification
tasks.Comment: To appear in the INTERSPEECH 2018 Proceeding
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