26 research outputs found
KinshipGAN: Synthesizing of Kinship Faces From Family Photos by Regularizing a Deep Face Network
In this paper, we propose a kinship generator network that can synthesize a
possible child face by analyzing his/her parent's photo. For this purpose, we
focus on to handle the scarcity of kinship datasets throughout the paper by
proposing novel solutions in particular. To extract robust features, we
integrate a pre-trained face model to the kinship face generator. Moreover, the
generator network is regularized with an additional face dataset and
adversarial loss to decrease the overfitting of the limited samples. Lastly, we
adapt cycle-domain transformation to attain a more stable results. Experiments
are conducted on Families in the Wild (FIW) dataset. The experimental results
show that the contributions presented in the paper provide important
performance improvements compared to the baseline architecture and our proposed
method yields promising perceptual results.Comment: Accepted to IEEE ICIP 201
Image-based family verification in the wild
Facial image analysis has been an important subject of study in the communities of pat-
tern recognition and computer vision. Facial images contain much information about the
person they belong to: identity, age, gender, ethnicity, expression and many more. For that
reason, the analysis of facial images has many applications in real world problems such
as face recognition, age estimation, gender classification or facial expression recognition.
Visual kinship recognition is a new research topic in the scope of facial image analysis.
It is essential for many real-world applications. However, nowadays
there exist only a few practical vision systems capable to handle such tasks. Hence, vision
technology for kinship-based problems has not matured enough to be applied to real-
world problems. This leads to a concern of unsatisfactory performance when attempted
on real-world datasets.
Kinship verification is to determine pairwise kin relations for a pair of given images. It
can be viewed as a typical binary classification problem, i.e., a face pair is either related
by kinship or it is not. Prior research works have addressed kinship types
for which pre-existing datasets have provided images, annotations and a verification task
protocol. Namely, father-son, father-daughter, mother-son and mother-daughter.
The main objective of this Master work is the study and development of feature selection
and fusion for the problem of family verification from facial images.
To achieve this objective, there is a main tasks that can be addressed: perform a compara-
tive study on face descriptors that include classic descriptors as well as deep descriptors.
The main contributions of this Thesis work are:
1. Studying the state of the art of the problem of family verification in images.
2. Implementing and comparing several criteria that correspond to different face rep-
resentations (Local Binary Patterns (LBP), Histogram Oriented Gradients (HOG),
deep descriptors)
Learning Discriminative Features with Class Encoder
Deep neural networks usually benefit from unsupervised pre-training, e.g.
auto-encoders. However, the classifier further needs supervised fine-tuning
methods for good discrimination. Besides, due to the limits of full-connection,
the application of auto-encoders is usually limited to small, well aligned
images. In this paper, we incorporate the supervised information to propose a
novel formulation, namely class-encoder, whose training objective is to
reconstruct a sample from another one of which the labels are identical.
Class-encoder aims to minimize the intra-class variations in the feature space,
and to learn a good discriminative manifolds on a class scale. We impose the
class-encoder as a constraint into the softmax for better supervised training,
and extend the reconstruction on feature-level to tackle the parameter size
issue and translation issue. The experiments show that the class-encoder helps
to improve the performance on benchmarks of classification and face
recognition. This could also be a promising direction for fast training of face
recognition models.Comment: Accepted by CVPR2016 Workshop of Robust Features for Computer Visio