Boosted Independent Features for Face Expression Recognition

2005 ◽  
pp. 137-146 ◽  
Author(s):  
Lianghua He ◽  
Jianzhong Zhou ◽  
Die Hu ◽  
Cairong Zou ◽  
Li Zhao
Keyword(s):  
Expression Recognition ◽  
Face Expression ◽  
Face Expression Recognition ◽  
Independent Features

Concise CNN model for face expression recognition

2021 ◽  
pp. 1-9
Author(s):  
Harshadkumar B. Prajapati ◽  
Ankit S. Vyas ◽  
Vipul K. Dabhi
Keyword(s):  
Network Architecture ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Face Expression ◽  
Computational Overhead ◽  
Average Accuracy ◽  
Proposed Model ◽  
Face Expression Recognition ◽  
Expression Classification ◽  
Machine Interaction

Face expression recognition (FER) has gained very much attraction to researchers in the field of computer vision because of its major usefulness in security, robotics, and HMI (Human-Machine Interaction) systems. We propose a CNN (Convolutional Neural Network) architecture to address FER. To show the effectiveness of the proposed model, we evaluate the performance of the model on JAFFE dataset. We derive a concise CNN architecture to address the issue of expression classification. Objective of various experiments is to achieve convincing performance by reducing computational overhead. The proposed CNN model is very compact as compared to other state-of-the-art models. We could achieve highest accuracy of 97.10% and average accuracy of 90.43% for top 10 best runs without any pre-processing methods applied, which justifies the effectiveness of our model. Furthermore, we have also included visualization of CNN layers to observe the learning of CNN.

scholarly journals Analysis of RGB-D camera technologies for supporting different facial usage scenarios

2020 ◽  
Vol 79 (39-40) ◽  
pp. 29375-29398
Author(s):  
Luca Ulrich ◽  
Enrico Vezzetti ◽  
Sandro Moos ◽  
Federica Marcolin
Keyword(s):  
Correct Choice ◽  
Depth Information ◽  
Expression Recognition ◽  
Face Expression ◽  
Starting Point ◽  
3D Camera ◽  
Face Expression Recognition ◽  
3D Information ◽  
Face Morphology ◽  
New Algorithms

Abstract Recently a wide variety of applications has been developed integrating 3D functionalities. Advantages given by the possibility of relying on depth information allows the developers to design new algorithms and to improve the existing ones. In particular, for what concerns face morphology, 3D has led to the possibility to obtain face depth maps highly close to reality and consequently an improvement of the starting point for further analysis such as Face Detection, Face Authentication, Face Identification and Face Expression Recognition. The development of the aforementioned applications would have been impossible without the progress of sensor technologies for obtaining 3D information. Several solutions have been adopted over time. In this paper, emphasis is put on passive stereoscopy, structured light, time-of-flight (ToF) and active stereoscopy, namely the most used technologies for the cameras design and fulfilment according to the literature. The aim of this article is to investigate facial applications and to examine 3D camera technologies to suggest some guidelines for addressing the correct choice of a 3D sensor according to the application that has to be developed.

Face Expression Recognition through Broken Symmetries

2008 ◽  
Author(s):  
Vito Di Gesù ◽  
Bertrand Zavidovique ◽  
Marco Elio Tabacchi
Keyword(s):  
Expression Recognition ◽  
Broken Symmetries ◽  
Face Expression ◽  
Face Expression Recognition

Face Expression Recognition Using False Geodesic Distance

2013 ◽  
Author(s):  
Kezheng Lin ◽  
◽  
Weiyue Cheng ◽  
Jingtian Li ◽  
◽  
...  
Keyword(s):  
Geodesic Distance ◽  
Expression Recognition ◽  
Face Expression ◽  
Face Expression Recognition
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