2,753 research outputs found
Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural Networks
Ensemble methods, traditionally built with independently trained
de-correlated models, have proven to be efficient methods for reducing the
remaining residual generalization error, which results in robust and accurate
methods for real-world applications. In the context of deep learning, however,
training an ensemble of deep networks is costly and generates high redundancy
which is inefficient. In this paper, we present experiments on Ensembles with
Shared Representations (ESRs) based on convolutional networks to demonstrate,
quantitatively and qualitatively, their data processing efficiency and
scalability to large-scale datasets of facial expressions. We show that
redundancy and computational load can be dramatically reduced by varying the
branching level of the ESR without loss of diversity and generalization power,
which are both important for ensemble performance. Experiments on large-scale
datasets suggest that ESRs reduce the remaining residual generalization error
on the AffectNet and FER+ datasets, reach human-level performance, and
outperform state-of-the-art methods on facial expression recognition in the
wild using emotion and affect concepts.Comment: Accepted at the Thirty-Fourth AAAI Conference on Artificial
Intelligence (AAAI-20), 1-1, New York, US
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
A Taxonomy of Deep Convolutional Neural Nets for Computer Vision
Traditional architectures for solving computer vision problems and the degree
of success they enjoyed have been heavily reliant on hand-crafted features.
However, of late, deep learning techniques have offered a compelling
alternative -- that of automatically learning problem-specific features. With
this new paradigm, every problem in computer vision is now being re-examined
from a deep learning perspective. Therefore, it has become important to
understand what kind of deep networks are suitable for a given problem.
Although general surveys of this fast-moving paradigm (i.e. deep-networks)
exist, a survey specific to computer vision is missing. We specifically
consider one form of deep networks widely used in computer vision -
convolutional neural networks (CNNs). We start with "AlexNet" as our base CNN
and then examine the broad variations proposed over time to suit different
applications. We hope that our recipe-style survey will serve as a guide,
particularly for novice practitioners intending to use deep-learning techniques
for computer vision.Comment: Published in Frontiers in Robotics and AI (http://goo.gl/6691Bm
- …