8,011 research outputs found
Smile detection in the wild based on transfer learning
Smile detection from unconstrained facial images is a specialized and
challenging problem. As one of the most informative expressions, smiles convey
basic underlying emotions, such as happiness and satisfaction, which lead to
multiple applications, e.g., human behavior analysis and interactive
controlling. Compared to the size of databases for face recognition, far less
labeled data is available for training smile detection systems. To leverage the
large amount of labeled data from face recognition datasets and to alleviate
overfitting on smile detection, an efficient transfer learning-based smile
detection approach is proposed in this paper. Unlike previous works which use
either hand-engineered features or train deep convolutional networks from
scratch, a well-trained deep face recognition model is explored and fine-tuned
for smile detection in the wild. Three different models are built as a result
of fine-tuning the face recognition model with different inputs, including
aligned, unaligned and grayscale images generated from the GENKI-4K dataset.
Experiments show that the proposed approach achieves improved state-of-the-art
performance. Robustness of the model to noise and blur artifacts is also
evaluated in this paper
Learning Residual Images for Face Attribute Manipulation
Face attributes are interesting due to their detailed description of human
faces. Unlike prior researches working on attribute prediction, we address an
inverse and more challenging problem called face attribute manipulation which
aims at modifying a face image according to a given attribute value. Instead of
manipulating the whole image, we propose to learn the corresponding residual
image defined as the difference between images before and after the
manipulation. In this way, the manipulation can be operated efficiently with
modest pixel modification. The framework of our approach is based on the
Generative Adversarial Network. It consists of two image transformation
networks and a discriminative network. The transformation networks are
responsible for the attribute manipulation and its dual operation and the
discriminative network is used to distinguish the generated images from real
images. We also apply dual learning to allow transformation networks to learn
from each other. Experiments show that residual images can be effectively
learned and used for attribute manipulations. The generated images remain most
of the details in attribute-irrelevant areas
Learning Compositional Visual Concepts with Mutual Consistency
Compositionality of semantic concepts in image synthesis and analysis is
appealing as it can help in decomposing known and generatively recomposing
unknown data. For instance, we may learn concepts of changing illumination,
geometry or albedo of a scene, and try to recombine them to generate physically
meaningful, but unseen data for training and testing. In practice however we
often do not have samples from the joint concept space available: We may have
data on illumination change in one data set and on geometric change in another
one without complete overlap. We pose the following question: How can we learn
two or more concepts jointly from different data sets with mutual consistency
where we do not have samples from the full joint space? We present a novel
answer in this paper based on cyclic consistency over multiple concepts,
represented individually by generative adversarial networks (GANs). Our method,
ConceptGAN, can be understood as a drop in for data augmentation to improve
resilience for real world applications. Qualitative and quantitative
evaluations demonstrate its efficacy in generating semantically meaningful
images, as well as one shot face verification as an example application.Comment: 10 pages, 8 figures, 4 tables, CVPR 201
- …