13 research outputs found
Kernelized dense layers for facial expression recognition
Fully connected layer is an essential component of Convolutional Neural
Networks (CNNs), which demonstrates its efficiency in computer vision tasks.
The CNN process usually starts with convolution and pooling layers that first
break down the input images into features, and then analyze them independently.
The result of this process feeds into a fully connected neural network
structure which drives the final classification decision. In this paper, we
propose a Kernelized Dense Layer (KDL) which captures higher order feature
interactions instead of conventional linear relations. We apply this method to
Facial Expression Recognition (FER) and evaluate its performance on RAF,
FER2013 and ExpW datasets. The experimental results demonstrate the benefits of
such layer and show that our model achieves competitive results with respect to
the state-of-the-art approaches
AUTO3D: Novel view synthesis through unsupervisely learned variational viewpoint and global 3D representation
This paper targets on learning-based novel view synthesis from a single or
limited 2D images without the pose supervision. In the viewer-centered
coordinates, we construct an end-to-end trainable conditional variational
framework to disentangle the unsupervisely learned relative-pose/rotation and
implicit global 3D representation (shape, texture and the origin of
viewer-centered coordinates, etc.). The global appearance of the 3D object is
given by several appearance-describing images taken from any number of
viewpoints. Our spatial correlation module extracts a global 3D representation
from the appearance-describing images in a permutation invariant manner. Our
system can achieve implicitly 3D understanding without explicitly 3D
reconstruction. With an unsupervisely learned viewer-centered
relative-pose/rotation code, the decoder can hallucinate the novel view
continuously by sampling the relative-pose in a prior distribution. In various
applications, we demonstrate that our model can achieve comparable or even
better results than pose/3D model-supervised learning-based novel view
synthesis (NVS) methods with any number of input views.Comment: ECCV 202
Smart classroom monitoring using novel real-time facial expression recognition system
Featured Application: The proposed automatic emotion recognition system has been deployed
in the classroom environment (education) but it can be used anywhere to monitor the emotions
of humans, i.e., health, banking, industries, social welfare etc.
Abstract: Emotions play a vital role in education. Technological advancement in computer vision
using deep learning models has improved automatic emotion recognition. In this study, a real-time
automatic emotion recognition system is developed incorporating novel salient facial features for
classroom assessment using a deep learning model. The proposed novel facial features for each
emotion are initially detected using HOG for face recognition, and automatic emotion recognition is
then performed by training a convolutional neural network (CNN) that takes real-time input from
a camera deployed in the classroom. The proposed emotion recognition system will analyze the
facial expressions of each student during learning. The selected emotional states are happiness,
sadness, and fear along with the cognitive–emotional states of satisfaction, dissatisfaction, and
concentration. The selected emotional states are tested against selected variables gender, department,
lecture time, seating positions, and the difficulty of a subject. The proposed system contributes to
improve classroom learning.Web of Science1223art. no. 1213