Deep learning for behaviour recognition in surveillance applications

2019 ◽  
Author(s):  
Maria Andersson
Keyword(s):  
Deep Learning ◽  
Behaviour Recognition ◽  
Surveillance Applications

Finding Facial Emotions From the Clutter Scenes Using Zernike Moments-Based Convolutional Neural Networks

2021 ◽  
pp. 241-265
Author(s):  
Wencan Zhong ◽  
Vijayalakshmi G. V. Mahesh ◽  
Alex Noel Joseph Raj ◽  
Nersisson Ruban
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Face Recognition ◽  
Facial Expression ◽  
Convolutional Neural Networks ◽  
Zernike Moments ◽  
The Other ◽  
Recognition Systems ◽  
Facial Images ◽  
Surveillance Applications

Finding faces in the clutter scenes is a challenging task in automatic face recognition systems as facial images are subjected to changes in the illumination, facial expression, orientation, and occlusions. Also, in the cluttered scenes, faces are not completely visible and detecting them is essential as it is significant in surveillance applications to study the mood of the crowd. This chapter utilizes the deep learning methods to understand the cluttered scenes to find the faces and discriminate them into partial and full faces. The work proves that MTCNN used for detecting the faces and Zernike moments-based kernels employed in CNN for classifying the faces into partial and full takes advantage in delivering a notable performance as compared to the other techniques. Considering the limitation of recognition on partial face emotions, only the full faces are preserved, and further, the KDEF dataset is modified by MTCNN to detect only faces and classify them into four emotions. PatternNet is utilized to train and test the modified dataset to improve the accuracy of the results.

Sign in / Sign up

Export Citation Format

Share Document