1,893 research outputs found
Building Deep Networks on Grassmann Manifolds
Learning representations on Grassmann manifolds is popular in quite a few
visual recognition tasks. In order to enable deep learning on Grassmann
manifolds, this paper proposes a deep network architecture by generalizing the
Euclidean network paradigm to Grassmann manifolds. In particular, we design
full rank mapping layers to transform input Grassmannian data to more desirable
ones, exploit re-orthonormalization layers to normalize the resulting matrices,
study projection pooling layers to reduce the model complexity in the
Grassmannian context, and devise projection mapping layers to respect
Grassmannian geometry and meanwhile achieve Euclidean forms for regular output
layers. To train the Grassmann networks, we exploit a stochastic gradient
descent setting on manifolds of the connection weights, and study a matrix
generalization of backpropagation to update the structured data. The
evaluations on three visual recognition tasks show that our Grassmann networks
have clear advantages over existing Grassmann learning methods, and achieve
results comparable with state-of-the-art approaches.Comment: AAAI'18 pape
Automatic Analysis of Facial Expressions Based on Deep Covariance Trajectories
In this paper, we propose a new approach for facial expression recognition
using deep covariance descriptors. The solution is based on the idea of
encoding local and global Deep Convolutional Neural Network (DCNN) features
extracted from still images, in compact local and global covariance
descriptors. The space geometry of the covariance matrices is that of Symmetric
Positive Definite (SPD) matrices. By conducting the classification of static
facial expressions using Support Vector Machine (SVM) with a valid Gaussian
kernel on the SPD manifold, we show that deep covariance descriptors are more
effective than the standard classification with fully connected layers and
softmax. Besides, we propose a completely new and original solution to model
the temporal dynamic of facial expressions as deep trajectories on the SPD
manifold. As an extension of the classification pipeline of covariance
descriptors, we apply SVM with valid positive definite kernels derived from
global alignment for deep covariance trajectories classification. By performing
extensive experiments on the Oulu-CASIA, CK+, and SFEW datasets, we show that
both the proposed static and dynamic approaches achieve state-of-the-art
performance for facial expression recognition outperforming many recent
approaches.Comment: A preliminary version of this work appeared in "Otberdout N, Kacem A,
Daoudi M, Ballihi L, Berretti S. Deep Covariance Descriptors for Facial
Expression Recognition, in British Machine Vision Conference 2018, BMVC 2018,
Northumbria University, Newcastle, UK, September 3-6, 2018. ; 2018 :159."
arXiv admin note: substantial text overlap with arXiv:1805.0386
Recognising facial expressions in video sequences
We introduce a system that processes a sequence of images of a front-facing human face and recognises a set of facial expressions. We use an efficient appearance-based face tracker to locate the face in the image sequence and estimate the deformation of its non-rigid components. The tracker works in real-time. It is robust to strong illumination changes and factors out changes in appearance caused by illumination from changes due to face deformation. We adopt a model-based approach for facial expression recognition. In our model, an image of a face is represented by a point in a deformation space. The variability of the classes of images associated to facial expressions are represented by a set of samples which model a low-dimensional manifold in the space of deformations. We introduce a probabilistic procedure based on a nearest-neighbour approach to combine the information provided by the incoming image sequence with the prior information stored in the expression manifold in order to compute a posterior probability associated to a facial expression. In the experiments conducted we show that this system is able to work in an unconstrained environment with strong changes in illumination and face location. It achieves an 89\% recognition rate in a set of 333 sequences from the Cohn-Kanade data base
Graph-based classification of multiple observation sets
We consider the problem of classification of an object given multiple
observations that possibly include different transformations. The possible
transformations of the object generally span a low-dimensional manifold in the
original signal space. We propose to take advantage of this manifold structure
for the effective classification of the object represented by the observation
set. In particular, we design a low complexity solution that is able to exploit
the properties of the data manifolds with a graph-based algorithm. Hence, we
formulate the computation of the unknown label matrix as a smoothing process on
the manifold under the constraint that all observations represent an object of
one single class. It results into a discrete optimization problem, which can be
solved by an efficient and low complexity algorithm. We demonstrate the
performance of the proposed graph-based algorithm in the classification of sets
of multiple images. Moreover, we show its high potential in video-based face
recognition, where it outperforms state-of-the-art solutions that fall short of
exploiting the manifold structure of the face image data sets.Comment: New content adde
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