26,374 research outputs found
Child Face Age-Progression via Deep Feature Aging
Given a gallery of face images of missing children, state-of-the-art face
recognition systems fall short in identifying a child (probe) recovered at a
later age. We propose a feature aging module that can age-progress deep face
features output by a face matcher. In addition, the feature aging module guides
age-progression in the image space such that synthesized aged faces can be
utilized to enhance longitudinal face recognition performance of any face
matcher without requiring any explicit training. For time lapses larger than 10
years (the missing child is found after 10 or more years), the proposed
age-progression module improves the closed-set identification accuracy of
FaceNet from 16.53% to 21.44% and CosFace from 60.72% to 66.12% on a child
celebrity dataset, namely ITWCC. The proposed method also outperforms
state-of-the-art approaches with a rank-1 identification rate of 95.91%,
compared to 94.91%, on a public aging dataset, FG-NET, and 99.58%, compared to
99.50%, on CACD-VS. These results suggest that aging face features enhances the
ability to identify young children who are possible victims of child
trafficking or abduction.Comment: arXiv admin note: substantial text overlap with arXiv:1911.0753
Modeling of Facial Aging and Kinship: A Survey
Computational facial models that capture properties of facial cues related to
aging and kinship increasingly attract the attention of the research community,
enabling the development of reliable methods for age progression, age
estimation, age-invariant facial characterization, and kinship verification
from visual data. In this paper, we review recent advances in modeling of
facial aging and kinship. In particular, we provide an up-to date, complete
list of available annotated datasets and an in-depth analysis of geometric,
hand-crafted, and learned facial representations that are used for facial aging
and kinship characterization. Moreover, evaluation protocols and metrics are
reviewed and notable experimental results for each surveyed task are analyzed.
This survey allows us to identify challenges and discuss future research
directions for the development of robust facial models in real-world
conditions
Personalized and Occupational-aware Age Progression by Generative Adversarial Networks
Face age progression, which aims to predict the future looks, is important
for various applications and has been received considerable attentions.
Existing methods and datasets are limited in exploring the effects of
occupations which may influence the personal appearances. In this paper, we
firstly introduce an occupational face aging dataset for studying the
influences of occupations on the appearances. It includes five occupations,
which enables the development of new algorithms for age progression and
facilitate future researches. Second, we propose a new occupational-aware
adversarial face aging network, which learns human aging process under
different occupations. Two factors are taken into consideration in our aging
process: personality-preserving and visually plausible texture change for
different occupations. We propose personalized network with personalized loss
in deep autoencoder network for keeping personalized facial characteristics,
and occupational-aware adversarial network with occupational-aware adversarial
loss for obtaining more realistic texture changes. Experimental results well
demonstrate the advantages of the proposed method by comparing with other
state-of-the-arts age progression methods.Comment: 9 pages, 10 figure
Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches
Face Aging has raised considerable attentions and interest from the computer
vision community in recent years. Numerous approaches ranging from purely image
processing techniques to deep learning structures have been proposed in
literature. In this paper, we aim to give a review of recent developments of
modern deep learning based approaches, i.e. Deep Generative Models, for Face
Aging task. Their structures, formulation, learning algorithms as well as
synthesized results are also provided with systematic discussions. Moreover,
the aging databases used in most methods to learn the aging process are also
reviewed
Face Aging with Contextual Generative Adversarial Nets
Face aging, which renders aging faces for an input face, has attracted
extensive attention in the multimedia research. Recently, several conditional
Generative Adversarial Nets (GANs) based methods have achieved great success.
They can generate images fitting the real face distributions conditioned on
each individual age group. However, these methods fail to capture the
transition patterns, e.g., the gradual shape and texture changes between
adjacent age groups. In this paper, we propose a novel Contextual Generative
Adversarial Nets (C-GANs) to specifically take it into consideration. The
C-GANs consists of a conditional transformation network and two discriminative
networks. The conditional transformation network imitates the aging procedure
with several specially designed residual blocks. The age discriminative network
guides the synthesized face to fit the real conditional distribution. The
transition pattern discriminative network is novel, aiming to distinguish the
real transition patterns with the fake ones. It serves as an extra
regularization term for the conditional transformation network, ensuring the
generated image pairs to fit the corresponding real transition pattern
distribution. Experimental results demonstrate the proposed framework produces
appealing results by comparing with the state-of-the-art and ground truth. We
also observe performance gain for cross-age face verification.Comment: accepted at ACM Multimedia 201
Learning from Longitudinal Face Demonstration - Where Tractable Deep Modeling Meets Inverse Reinforcement Learning
This paper presents a novel Subject-dependent Deep Aging Path (SDAP), which
inherits the merits of both Generative Probabilistic Modeling and Inverse
Reinforcement Learning to model the facial structures and the longitudinal face
aging process of a given subject. The proposed SDAP is optimized using
tractable log-likelihood objective functions with Convolutional Neural Networks
(CNNs) based deep feature extraction. Instead of applying a fixed aging
development path for all input faces and subjects, SDAP is able to provide the
most appropriate aging development path for individual subject that optimizes
the reward aging formulation. Unlike previous methods that can take only one
image as the input, SDAP further allows multiple images as inputs, i.e. all
information of a subject at either the same or different ages, to produce the
optimal aging path for the given subject. Finally, SDAP allows efficiently
synthesizing in-the-wild aging faces. The proposed model is experimented in
both tasks of face aging synthesis and cross-age face verification. The
experimental results consistently show SDAP achieves the state-of-the-art
performance on numerous face aging databases, i.e. FG-NET, MORPH, AginG Faces
in the Wild (AGFW), and Cross-Age Celebrity Dataset (CACD). Furthermore, we
also evaluate the performance of SDAP on large-scale Megaface challenge to
demonstrate the advantages of the proposed solution
Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition
As facial appearance is subject to significant intra-class variations caused
by the aging process over time, age-invariant face recognition (AIFR) remains a
major challenge in face recognition community. To reduce the intra-class
discrepancy caused by the aging, in this paper we propose a novel approach
(namely, Orthogonal Embedding CNNs, or OE-CNNs) to learn the age-invariant deep
face features. Specifically, we decompose deep face features into two
orthogonal components to represent age-related and identity-related features.
As a result, identity-related features that are robust to aging are then used
for AIFR. Besides, for complementing the existing cross-age datasets and
advancing the research in this field, we construct a brand-new large-scale
Cross-Age Face dataset (CAF). Extensive experiments conducted on the three
public domain face aging datasets (MORPH Album 2, CACD-VS and FG-NET) have
shown the effectiveness of the proposed approach and the value of the
constructed CAF dataset on AIFR. Benchmarking our algorithm on one of the most
popular general face recognition (GFR) dataset LFW additionally demonstrates
the comparable generalization performance on GFR
Temporal Non-Volume Preserving Approach to Facial Age-Progression and Age-Invariant Face Recognition
Modeling the long-term facial aging process is extremely challenging due to
the presence of large and non-linear variations during the face development
stages. In order to efficiently address the problem, this work first decomposes
the aging process into multiple short-term stages. Then, a novel generative
probabilistic model, named Temporal Non-Volume Preserving (TNVP)
transformation, is presented to model the facial aging process at each stage.
Unlike Generative Adversarial Networks (GANs), which requires an empirical
balance threshold, and Restricted Boltzmann Machines (RBM), an intractable
model, our proposed TNVP approach guarantees a tractable density function,
exact inference and evaluation for embedding the feature transformations
between faces in consecutive stages. Our model shows its advantages not only in
capturing the non-linear age related variance in each stage but also producing
a smooth synthesis in age progression across faces. Our approach can model any
face in the wild provided with only four basic landmark points. Moreover, the
structure can be transformed into a deep convolutional network while keeping
the advantages of probabilistic models with tractable log-likelihood density
estimation. Our method is evaluated in both terms of synthesizing
age-progressed faces and cross-age face verification and consistently shows the
state-of-the-art results in various face aging databases, i.e. FG-NET, MORPH,
AginG Faces in the Wild (AGFW), and Cross-Age Celebrity Dataset (CACD). A
large-scale face verification on Megaface challenge 1 is also performed to
further show the advantages of our proposed approach
Large age-gap face verification by feature injection in deep networks
This paper introduces a new method for face verification across large age
gaps and also a dataset containing variations of age in the wild, the Large
Age-Gap (LAG) dataset, with images ranging from child/young to adult/old. The
proposed method exploits a deep convolutional neural network (DCNN) pre-trained
for the face recognition task on a large dataset and then fine-tuned for the
large age-gap face verification task. Finetuning is performed in a Siamese
architecture using a contrastive loss function. A feature injection layer is
introduced to boost verification accuracy, showing the ability of the DCNN to
learn a similarity metric leveraging external features. Experimental results on
the LAG dataset show that our method is able to outperform the face
verification solutions in the state of the art considered.Comment: Submitte
Decorrelated Adversarial Learning for Age-Invariant Face Recognition
There has been an increasing research interest in age-invariant face
recognition. However, matching faces with big age gaps remains a challenging
problem, primarily due to the significant discrepancy of face appearances
caused by aging. To reduce such a discrepancy, in this paper we propose a novel
algorithm to remove age-related components from features mixed with both
identity and age information. Specifically, we factorize a mixed face feature
into two uncorrelated components: identity-dependent component and
age-dependent component, where the identity-dependent component includes
information that is useful for face recognition. To implement this idea, we
propose the Decorrelated Adversarial Learning (DAL) algorithm, where a
Canonical Mapping Module (CMM) is introduced to find the maximum correlation
between the paired features generated by a backbone network, while the backbone
network and the factorization module are trained to generate features reducing
the correlation. Thus, the proposed model learns the decomposed features of age
and identity whose correlation is significantly reduced. Simultaneously, the
identity-dependent feature and the age-dependent feature are respectively
supervised by ID and age preserving signals to ensure that they both contain
the correct information. Extensive experiments are conducted on popular
public-domain face aging datasets (FG-NET, MORPH Album 2, and CACD-VS) to
demonstrate the effectiveness of the proposed approach
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