2,684 research outputs found
Micro-expression Recognition using Spatiotemporal Texture Map and Motion Magnification
Micro-expressions are short-lived, rapid facial expressions that are exhibited by individuals when they are in high stakes situations. Studying these micro-expressions is important as these cannot be modified by an individual and hence offer us a peek into what the individual is actually feeling and thinking as opposed to what he/she is trying to portray. The spotting and recognition of micro-expressions has applications in the fields of criminal investigation, psychotherapy, education etc. However due to micro-expressions’ short-lived and rapid nature; spotting, recognizing and classifying them is a major challenge. In this paper, we design a hybrid approach for spotting and recognizing micro-expressions by utilizing motion magnification using Eulerian Video Magnification and Spatiotemporal Texture Map (STTM). The validation of this approach was done on the spontaneous micro-expression dataset, CASMEII in comparison with the baseline. This approach achieved an accuracy of 80% viz. an increase by 5% as compared to the existing baseline by utilizing 10-fold cross validation using Support Vector Machines (SVM) with a linear kernel
Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images Using Graph Convolutional Networks
3D Morphable Model (3DMM) based methods have achieved great success in
recovering 3D face shapes from single-view images. However, the facial textures
recovered by such methods lack the fidelity as exhibited in the input images.
Recent work demonstrates high-quality facial texture recovering with generative
networks trained from a large-scale database of high-resolution UV maps of face
textures, which is hard to prepare and not publicly available. In this paper,
we introduce a method to reconstruct 3D facial shapes with high-fidelity
textures from single-view images in-the-wild, without the need to capture a
large-scale face texture database. The main idea is to refine the initial
texture generated by a 3DMM based method with facial details from the input
image. To this end, we propose to use graph convolutional networks to
reconstruct the detailed colors for the mesh vertices instead of reconstructing
the UV map. Experiments show that our method can generate high-quality results
and outperforms state-of-the-art methods in both qualitative and quantitative
comparisons.Comment: Accepted to CVPR 2020. The source code is available at
https://github.com/FuxiCV/3D-Face-GCN
Face analysis using curve edge maps
This paper proposes an automatic and real-time system for face analysis, usable in visual communication applications. In this approach, faces are represented with Curve Edge Maps, which are collections of polynomial segments with a convex region. The segments are extracted from edge pixels using an adaptive incremental linear-time fitting algorithm, which is based on constructive polynomial fitting. The face analysis system considers face tracking, face recognition and facial feature detection, using Curve Edge Maps driven by histograms of intensities and histograms of relative positions. When applied to different face databases and video sequences, the average face recognition rate is 95.51%, the average facial feature detection rate is 91.92% and the accuracy in location of the facial features is 2.18% in terms of the size of the face, which is comparable with or better than the results in literature. However, our method has the advantages of simplicity, real-time performance and extensibility to the different aspects of face analysis, such as recognition of facial expressions and talking
3D Face Synthesis with KINECT
This work describes the process of face synthesis by image morphing from less expensive 3D sensors such as KINECT that are prone to sensor noise. Its main aim is to create a useful face database for future face recognition studies.Peer reviewe
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
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