5 research outputs found
Facial Aging and Rejuvenation by Conditional Multi-Adversarial Autoencoder with Ordinal Regression
Facial aging and facial rejuvenation analyze a given face photograph to
predict a future look or estimate a past look of the person. To achieve this,
it is critical to preserve human identity and the corresponding aging
progression and regression with high accuracy. However, existing methods cannot
simultaneously handle these two objectives well. We propose a novel generative
adversarial network based approach, named the Conditional Multi-Adversarial
AutoEncoder with Ordinal Regression (CMAAE-OR). It utilizes an age estimation
technique to control the aging accuracy and takes a high-level feature
representation to preserve personalized identity. Specifically, the face is
first mapped to a latent vector through a convolutional encoder. The latent
vector is then projected onto the face manifold conditional on the age through
a deconvolutional generator. The latent vector preserves personalized face
features and the age controls facial aging and rejuvenation. A discriminator
and an ordinal regression are imposed on the encoder and the generator in
tandem, making the generated face images to be more photorealistic while
simultaneously exhibiting desirable aging effects. Besides, a high-level
feature representation is utilized to preserve personalized identity of the
generated face. Experiments on two benchmark datasets demonstrate appealing
performance of the proposed method over the state-of-the-art
PFA-GAN: Progressive Face Aging with Generative Adversarial Network
Face aging is to render a given face to predict its future appearance, which
plays an important role in the information forensics and security field as the
appearance of the face typically varies with age. Although impressive results
have been achieved with conditional generative adversarial networks (cGANs),
the existing cGANs-based methods typically use a single network to learn
various aging effects between any two different age groups. However, they
cannot simultaneously meet three essential requirements of face aging --
including image quality, aging accuracy, and identity preservation -- and
usually generate aged faces with strong ghost artifacts when the age gap
becomes large. Inspired by the fact that faces gradually age over time, this
paper proposes a novel progressive face aging framework based on generative
adversarial network (PFA-GAN) to mitigate these issues. Unlike the existing
cGANs-based methods, the proposed framework contains several sub-networks to
mimic the face aging process from young to old, each of which only learns some
specific aging effects between two adjacent age groups. The proposed framework
can be trained in an end-to-end manner to eliminate accumulative artifacts and
blurriness. Moreover, this paper introduces an age estimation loss to take into
account the age distribution for an improved aging accuracy, and proposes to
use the Pearson correlation coefficient as an evaluation metric measuring the
aging smoothness for face aging methods. Extensively experimental results
demonstrate superior performance over existing (c)GANs-based methods, including
the state-of-the-art one, on two benchmarked datasets. The source code is
available at~\url{https://github.com/Hzzone/PFA-GAN}
C3AE: Exploring the Limits of Compact Model for Age Estimation
Age estimation is a classic learning problem in computer vision. Many larger
and deeper CNNs have been proposed with promising performance, such as AlexNet,
VggNet, GoogLeNet and ResNet. However, these models are not practical for the
embedded/mobile devices. Recently, MobileNets and ShuffleNets have been
proposed to reduce the number of parameters, yielding lightweight models.
However, their representation has been weakened because of the adoption of
depth-wise separable convolution. In this work, we investigate the limits of
compact model for small-scale image and propose an extremely Compact yet
efficient Cascade Context-based Age Estimation model(C3AE). This model
possesses only 1/9 and 1/2000 parameters compared with MobileNets/ShuffleNets
and VggNet, while achieves competitive performance. In particular, we re-define
age estimation problem by two-points representation, which is implemented by a
cascade model. Moreover, to fully utilize the facial context information,
multi-branch CNN network is proposed to aggregate multi-scale context.
Experiments are carried out on three age estimation datasets. The
state-of-the-art performance on compact model has been achieved with a
relatively large margin.Comment: accepted by cvpr201
Fair and accurate age prediction using distribution aware data curation and augmentation
Deep learning-based facial recognition systems have experienced increased
media attention due to exhibiting unfair behavior. Large enterprises, such as
IBM, shut down their facial recognition and age prediction systems as a
consequence. Age prediction is an especially difficult application with the
issue of fairness remaining an open research problem (e.g., predicting age for
different ethnicity equally accurate). One of the main causes of unfair
behavior in age prediction methods lies in the distribution and diversity of
the training data. In this work, we present two novel approaches for dataset
curation and data augmentation in order to increase fairness through balanced
feature curation and increase diversity through distribution aware
augmentation. To achieve this, we introduce out-of-distribution detection to
the facial recognition domain which is used to select the data most relevant to
the deep neural network's (DNN) task when balancing the data among age,
ethnicity, and gender. Our approach shows promising results. Our best-trained
DNN model outperformed all academic and industrial baselines in terms of
fairness by up to 4.92 times and also enhanced the DNN's ability to generalize
outperforming Amazon AWS and Microsoft Azure public cloud systems by 31.88% and
10.95%, respectively.Comment: Preprint, accepted at WACV'2
Ordinal Neural Network Transformation Models: Deep and interpretable regression models for ordinal outcomes
Outcomes with a natural order commonly occur in prediction tasks and
oftentimes the available input data are a mixture of complex data, like images,
and tabular predictors. Deep Learning (DL) methods are state-of-the-art for
image classification tasks but frequently treat ordinal outcomes as unordered
and lack interpretability. In contrast, classical ordinal regression models
consider the outcome's order and yield interpretable predictor effects but are
limited to tabular data. We present ordinal neural network transformation
models (ONTRAMs), which unite DL with classical ordinal regression methods.
ONTRAMs are a special case of transformation models and trade off flexibility
and interpretability by additively decomposing the transformation function into
terms for image and tabular data using jointly trained neural networks. We
discuss how to interpret model components for both tabular and image data. The
proposed ONTRAMs achieve on-par performance with common DL models while being
directly interpretable and more efficient in training.Comment: 37 pages (inkl. appendix, figures and literature), 11 figures in main
text, 5 figures in appendi