3,650 research outputs found
Machine Analysis of Facial Expressions
No abstract
Learning from Millions of 3D Scans for Large-scale 3D Face Recognition
Deep networks trained on millions of facial images are believed to be closely
approaching human-level performance in face recognition. However, open world
face recognition still remains a challenge. Although, 3D face recognition has
an inherent edge over its 2D counterpart, it has not benefited from the recent
developments in deep learning due to the unavailability of large training as
well as large test datasets. Recognition accuracies have already saturated on
existing 3D face datasets due to their small gallery sizes. Unlike 2D
photographs, 3D facial scans cannot be sourced from the web causing a
bottleneck in the development of deep 3D face recognition networks and
datasets. In this backdrop, we propose a method for generating a large corpus
of labeled 3D face identities and their multiple instances for training and a
protocol for merging the most challenging existing 3D datasets for testing. We
also propose the first deep CNN model designed specifically for 3D face
recognition and trained on 3.1 Million 3D facial scans of 100K identities. Our
test dataset comprises 1,853 identities with a single 3D scan in the gallery
and another 31K scans as probes, which is several orders of magnitude larger
than existing ones. Without fine tuning on this dataset, our network already
outperforms state of the art face recognition by over 10%. We fine tune our
network on the gallery set to perform end-to-end large scale 3D face
recognition which further improves accuracy. Finally, we show the efficacy of
our method for the open world face recognition problem.Comment: 11 page
Fully Automatic Expression-Invariant Face Correspondence
We consider the problem of computing accurate point-to-point correspondences
among a set of human face scans with varying expressions. Our fully automatic
approach does not require any manually placed markers on the scan. Instead, the
approach learns the locations of a set of landmarks present in a database and
uses this knowledge to automatically predict the locations of these landmarks
on a newly available scan. The predicted landmarks are then used to compute
point-to-point correspondences between a template model and the newly available
scan. To accurately fit the expression of the template to the expression of the
scan, we use as template a blendshape model. Our algorithm was tested on a
database of human faces of different ethnic groups with strongly varying
expressions. Experimental results show that the obtained point-to-point
correspondence is both highly accurate and consistent for most of the tested 3D
face models
Dense 3D Face Correspondence
We present an algorithm that automatically establishes dense correspondences
between a large number of 3D faces. Starting from automatically detected sparse
correspondences on the outer boundary of 3D faces, the algorithm triangulates
existing correspondences and expands them iteratively by matching points of
distinctive surface curvature along the triangle edges. After exhausting
keypoint matches, further correspondences are established by generating evenly
distributed points within triangles by evolving level set geodesic curves from
the centroids of large triangles. A deformable model (K3DM) is constructed from
the dense corresponded faces and an algorithm is proposed for morphing the K3DM
to fit unseen faces. This algorithm iterates between rigid alignment of an
unseen face followed by regularized morphing of the deformable model. We have
extensively evaluated the proposed algorithms on synthetic data and real 3D
faces from the FRGCv2, Bosphorus, BU3DFE and UND Ear databases using
quantitative and qualitative benchmarks. Our algorithm achieved dense
correspondences with a mean localisation error of 1.28mm on synthetic faces and
detected anthropometric landmarks on unseen real faces from the FRGCv2
database with 3mm precision. Furthermore, our deformable model fitting
algorithm achieved 98.5% face recognition accuracy on the FRGCv2 and 98.6% on
Bosphorus database. Our dense model is also able to generalize to unseen
datasets.Comment: 24 Pages, 12 Figures, 6 Tables and 3 Algorithm
Machine Understanding of Human Behavior
A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior
Baseline CNN structure analysis for facial expression recognition
We present a baseline convolutional neural network (CNN) structure and image
preprocessing methodology to improve facial expression recognition algorithm
using CNN. To analyze the most efficient network structure, we investigated
four network structures that are known to show good performance in facial
expression recognition. Moreover, we also investigated the effect of input
image preprocessing methods. Five types of data input (raw, histogram
equalization, isotropic smoothing, diffusion-based normalization, difference of
Gaussian) were tested, and the accuracy was compared. We trained 20 different
CNN models (4 networks x 5 data input types) and verified the performance of
each network with test images from five different databases. The experiment
result showed that a three-layer structure consisting of a simple convolutional
and a max pooling layer with histogram equalization image input was the most
efficient. We describe the detailed training procedure and analyze the result
of the test accuracy based on considerable observation.Comment: 6 pages, RO-MAN2016 Conferenc
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