1,322 research outputs found
Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets
A data augmentation methodology is presented and applied to generate a large
dataset of off-axis iris regions and train a low-complexity deep neural
network. Although of low complexity the resulting network achieves a high level
of accuracy in iris region segmentation for challenging off-axis eye-patches.
Interestingly, this network is also shown to achieve high levels of performance
for regular, frontal, segmentation of iris regions, comparing favorably with
state-of-the-art techniques of significantly higher complexity. Due to its
lower complexity, this network is well suited for deployment in embedded
applications such as augmented and mixed reality headsets
A Double-Deep Spatio-Angular Learning Framework for Light Field based Face Recognition
Face recognition has attracted increasing attention due to its wide range of
applications, but it is still challenging when facing large variations in the
biometric data characteristics. Lenslet light field cameras have recently come
into prominence to capture rich spatio-angular information, thus offering new
possibilities for advanced biometric recognition systems. This paper proposes a
double-deep spatio-angular learning framework for light field based face
recognition, which is able to learn both texture and angular dynamics in
sequence using convolutional representations; this is a novel recognition
framework that has never been proposed before for either face recognition or
any other visual recognition task. The proposed double-deep learning framework
includes a long short-term memory (LSTM) recurrent network whose inputs are
VGG-Face descriptions that are computed using a VGG-Very-Deep-16 convolutional
neural network (CNN). The VGG-16 network uses different face viewpoints
rendered from a full light field image, which are organised as a pseudo-video
sequence. A comprehensive set of experiments has been conducted with the
IST-EURECOM light field face database, for varied and challenging recognition
tasks. Results show that the proposed framework achieves superior face
recognition performance when compared to the state-of-the-art.Comment: Submitted to IEEE Transactions on Circuits and Systems for Video
Technolog
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