105,238 research outputs found
Comparative study of human age estimation based on hand-crafted and deep face features
In the past few years, human facial age estimation has drawn a lot of attention
in the computer vision and pattern recognition communities because of its important
applications in age-based image retrieval, security control and surveillance, biomet-
rics, human-computer interaction (HCI) and social robotics. In connection with these
investigations, estimating the age of a person from the numerical analysis of his/her
face image is a relatively new topic. Also, in problems such as Image Classification
the Deep Neural Networks have given the best results in some areas including age
estimation.
In this work we use three hand-crafted features as well as five deep features
that can be obtained from pre-trained deep convolutional neural networks. We do a
comparative study of the obtained age estimation results with these features
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks
Recently, skeleton based action recognition gains more popularity due to
cost-effective depth sensors coupled with real-time skeleton estimation
algorithms. Traditional approaches based on handcrafted features are limited to
represent the complexity of motion patterns. Recent methods that use Recurrent
Neural Networks (RNN) to handle raw skeletons only focus on the contextual
dependency in the temporal domain and neglect the spatial configurations of
articulated skeletons. In this paper, we propose a novel two-stream RNN
architecture to model both temporal dynamics and spatial configurations for
skeleton based action recognition. We explore two different structures for the
temporal stream: stacked RNN and hierarchical RNN. Hierarchical RNN is designed
according to human body kinematics. We also propose two effective methods to
model the spatial structure by converting the spatial graph into a sequence of
joints. To improve generalization of our model, we further exploit 3D
transformation based data augmentation techniques including rotation and
scaling transformation to transform the 3D coordinates of skeletons during
training. Experiments on 3D action recognition benchmark datasets show that our
method brings a considerable improvement for a variety of actions, i.e.,
generic actions, interaction activities and gestures.Comment: Accepted to IEEE International Conference on Computer Vision and
Pattern Recognition (CVPR) 201
Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks
Skeleton based action recognition distinguishes human actions using the
trajectories of skeleton joints, which provide a very good representation for
describing actions. Considering that recurrent neural networks (RNNs) with Long
Short-Term Memory (LSTM) can learn feature representations and model long-term
temporal dependencies automatically, we propose an end-to-end fully connected
deep LSTM network for skeleton based action recognition. Inspired by the
observation that the co-occurrences of the joints intrinsically characterize
human actions, we take the skeleton as the input at each time slot and
introduce a novel regularization scheme to learn the co-occurrence features of
skeleton joints. To train the deep LSTM network effectively, we propose a new
dropout algorithm which simultaneously operates on the gates, cells, and output
responses of the LSTM neurons. Experimental results on three human action
recognition datasets consistently demonstrate the effectiveness of the proposed
model.Comment: AAAI 2016 conferenc
- …