98,622 research outputs found
Deep Learning Face Attributes in the Wild
Predicting face attributes in the wild is challenging due to complex face
variations. We propose a novel deep learning framework for attribute prediction
in the wild. It cascades two CNNs, LNet and ANet, which are fine-tuned jointly
with attribute tags, but pre-trained differently. LNet is pre-trained by
massive general object categories for face localization, while ANet is
pre-trained by massive face identities for attribute prediction. This framework
not only outperforms the state-of-the-art with a large margin, but also reveals
valuable facts on learning face representation.
(1) It shows how the performances of face localization (LNet) and attribute
prediction (ANet) can be improved by different pre-training strategies.
(2) It reveals that although the filters of LNet are fine-tuned only with
image-level attribute tags, their response maps over entire images have strong
indication of face locations. This fact enables training LNet for face
localization with only image-level annotations, but without face bounding boxes
or landmarks, which are required by all attribute recognition works.
(3) It also demonstrates that the high-level hidden neurons of ANet
automatically discover semantic concepts after pre-training with massive face
identities, and such concepts are significantly enriched after fine-tuning with
attribute tags. Each attribute can be well explained with a sparse linear
combination of these concepts.Comment: To appear in International Conference on Computer Vision (ICCV) 201
Face Attribute Prediction Using Off-the-Shelf CNN Features
Predicting attributes from face images in the wild is a challenging computer
vision problem. To automatically describe face attributes from face containing
images, traditionally one needs to cascade three technical blocks --- face
localization, facial descriptor construction, and attribute classification ---
in a pipeline. As a typical classification problem, face attribute prediction
has been addressed using deep learning. Current state-of-the-art performance
was achieved by using two cascaded Convolutional Neural Networks (CNNs), which
were specifically trained to learn face localization and attribute description.
In this paper, we experiment with an alternative way of employing the power of
deep representations from CNNs. Combining with conventional face localization
techniques, we use off-the-shelf architectures trained for face recognition to
build facial descriptors. Recognizing that the describable face attributes are
diverse, our face descriptors are constructed from different levels of the CNNs
for different attributes to best facilitate face attribute prediction.
Experiments on two large datasets, LFWA and CelebA, show that our approach is
entirely comparable to the state-of-the-art. Our findings not only demonstrate
an efficient face attribute prediction approach, but also raise an important
question: how to leverage the power of off-the-shelf CNN representations for
novel tasks.Comment: In proceeding of 2016 International Conference on Biometrics (ICB
Learning Social Relation Traits from Face Images
Social relation defines the association, e.g, warm, friendliness, and
dominance, between two or more people. Motivated by psychological studies, we
investigate if such fine-grained and high-level relation traits can be
characterised and quantified from face images in the wild. To address this
challenging problem we propose a deep model that learns a rich face
representation to capture gender, expression, head pose, and age-related
attributes, and then performs pairwise-face reasoning for relation prediction.
To learn from heterogeneous attribute sources, we formulate a new network
architecture with a bridging layer to leverage the inherent correspondences
among these datasets. It can also cope with missing target attribute labels.
Extensive experiments show that our approach is effective for fine-grained
social relation learning in images and videos.Comment: To appear in International Conference on Computer Vision (ICCV) 201
Bias in Deep Learning and Applications to Face Analysis
Deep learning has fostered the progress in the field of face analysis, resulting in the integration of these models in multiple aspects of society. Even though the majority of research has focused on optimizing standard evaluation metrics, recent work has exposed the bias of such algorithms as well as the dangers of their unaccountable utilization.n this thesis, we explore the bias of deep learning models in the discriminative and the generative setting. We begin by investigating the bias of face analysis models with regards to different demographics. To this end, we collect KANFace, a large-scale video and image dataset of faces captured ``in-the-wild’'. The rich set of annotations allows us to expose the demographic bias of deep learning models, which we mitigate by utilizing adversarial learning to debias the deep representations. Furthermore, we explore neural augmentation as a strategy towards training fair classifiers. We propose a style-based multi-attribute transfer framework that is able to synthesize photo-realistic faces of the underrepresented demographics. This is achieved by introducing a multi-attribute extension to Adaptive Instance Normalisation that captures the multiplicative interactions between the representations of different attributes. Focusing on bias in gender recognition, we showcase the efficacy of the framework in training classifiers that are more fair compared to generative and fairness-aware methods.In the second part, we focus on bias in deep generative models. In particular, we start by studying the generalization of generative models on images of unseen attribute combinations. To this end, we extend the conditional Variational Autoencoder by introducing a multilinear conditioning framework. The proposed method is able to synthesize unseen attribute combinations by modeling the multiplicative interactions between the attributes. Lastly, in order to control protected attributes, we investigate controlled image generation without training on a labelled dataset. We leverage pre-trained Generative Adversarial Networks that are trained in an unsupervised fashion and exploit the clustering that occurs in the representation space of intermediate layers of the generator. We show that these clusters capture semantic attribute information and condition image synthesis on the cluster assignment using Implicit Maximum Likelihood Estimation.Open Acces
Leveraging Mid-Level Deep Representations For Predicting Face Attributes in the Wild
Predicting facial attributes from faces in the wild is very challenging due
to pose and lighting variations in the real world. The key to this problem is
to build proper feature representations to cope with these unfavourable
conditions. Given the success of Convolutional Neural Network (CNN) in image
classification, the high-level CNN feature, as an intuitive and reasonable
choice, has been widely utilized for this problem. In this paper, however, we
consider the mid-level CNN features as an alternative to the high-level ones
for attribute prediction. This is based on the observation that face attributes
are different: some of them are locally oriented while others are globally
defined. Our investigations reveal that the mid-level deep representations
outperform the prediction accuracy achieved by the (fine-tuned) high-level
abstractions. We empirically demonstrate that the midlevel representations
achieve state-of-the-art prediction performance on CelebA and LFWA datasets.
Our investigations also show that by utilizing the mid-level representations
one can employ a single deep network to achieve both face recognition and
attribute prediction.Comment: In proceedings of 2016 International Conference on Image Processing
(ICIP
Self-supervised learning of a facial attribute embedding from video
We propose a self-supervised framework for learning facial attributes by
simply watching videos of a human face speaking, laughing, and moving over
time. To perform this task, we introduce a network, Facial Attributes-Net
(FAb-Net), that is trained to embed multiple frames from the same video
face-track into a common low-dimensional space. With this approach, we make
three contributions: first, we show that the network can leverage information
from multiple source frames by predicting confidence/attention masks for each
frame; second, we demonstrate that using a curriculum learning regime improves
the learned embedding; finally, we demonstrate that the network learns a
meaningful face embedding that encodes information about head pose, facial
landmarks and facial expression, i.e. facial attributes, without having been
supervised with any labelled data. We are comparable or superior to
state-of-the-art self-supervised methods on these tasks and approach the
performance of supervised methods.Comment: To appear in BMVC 2018. Supplementary material can be found at
http://www.robots.ox.ac.uk/~vgg/research/unsup_learn_watch_faces/fabnet.htm
MobiFace: A Novel Dataset for Mobile Face Tracking in the Wild
Face tracking serves as the crucial initial step in mobile applications
trying to analyse target faces over time in mobile settings. However, this
problem has received little attention, mainly due to the scarcity of dedicated
face tracking benchmarks. In this work, we introduce MobiFace, the first
dataset for single face tracking in mobile situations. It consists of 80
unedited live-streaming mobile videos captured by 70 different smartphone users
in fully unconstrained environments. Over bounding boxes are manually
labelled. The videos are carefully selected to cover typical smartphone usage.
The videos are also annotated with 14 attributes, including 6 newly proposed
attributes and 8 commonly seen in object tracking. 36 state-of-the-art
trackers, including facial landmark trackers, generic object trackers and
trackers that we have fine-tuned or improved, are evaluated. The results
suggest that mobile face tracking cannot be solved through existing approaches.
In addition, we show that fine-tuning on the MobiFace training data
significantly boosts the performance of deep learning-based trackers,
suggesting that MobiFace captures the unique characteristics of mobile face
tracking. Our goal is to offer the community a diverse dataset to enable the
design and evaluation of mobile face trackers. The dataset, annotations and the
evaluation server will be on \url{https://mobiface.github.io/}.Comment: To appear on The 14th IEEE International Conference on Automatic Face
and Gesture Recognition (FG 2019
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