scholarly journals Design and Evaluation of a Hands-Free Video Game Controller for Individuals With Motor Impairments

2021 ◽  
Vol 3 ◽  
Author(s):  
Atieh Taheri ◽  
Ziv Weissman ◽  
Misha Sra
Keyword(s):  
Facial Expression ◽  
Video Game ◽  
User Satisfaction ◽  
Facial Expression Recognition ◽  
User Study ◽  
Control Method ◽  
Expression Recognition ◽  
Motor Impairments ◽  
Input Method ◽  
Input System

Over the past few decades, video gaming has evolved at a tremendous rate although game input methods have been slower to change. Game input methods continue to rely on two-handed control of the joystick and D-pad or the keyboard and mouse for simultaneously controlling player movement and camera actions. Bi-manual input poses a significant play impediment to those with severe motor impairments. In this work, we propose and evaluate a hands-free game input control method that uses real-time facial expression recognition. Through our novel input method, our goal is to enable and empower individuals with neurological and neuromuscular diseases, who may lack hand muscle control, to be able to independently play video games. To evaluate the usability and acceptance of our system, we conducted a remote user study with eight severely motor-impaired individuals. Our results indicate high user satisfaction and greater preference for our input system with participants rating the input system as easy to learn. With this work, we aim to highlight that facial expression recognition can be a valuable input method.

scholarly journals Personalized Movie Summarization Using Deep CNN-Assisted Facial Expression Recognition

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Ijaz Ul Haq ◽  
Amin Ullah ◽  
Khan Muhammad ◽  
Mi Young Lee ◽  
Sung Wook Baik
Keyword(s):  
Facial Expression ◽  
User Satisfaction ◽  
Facial Expression Recognition ◽  
Subjective Evaluation ◽  
Objective Evaluation ◽  
User Preference ◽  
Expression Recognition ◽  
Hollywood Movies ◽  
Deep Cnn ◽  
Movie Summarization

Personalized movie summarization is demand of the current era due to an exponential growth in movies production. The employed methods for movies summarization fail to satisfy the user’s requirements due to the subjective nature of movies data. Therefore, in this paper, we present a user-preference based movie summarization scheme. First, we segmented movie into shots using a novel entropy-based shots segmentation mechanism. Next, temporal saliency of shots is computed, resulting in highly salient shots in which character faces are detected. The resultant shots are then forward propagated to our trained deep CNN model for facial expression recognition (FER) to analyze the emotional state of the characters. The final summary is generated based on user-preferred emotional moments from the seven emotions, i.e., afraid, angry, disgust, happy, neutral, sad, and surprise. The subjective evaluation over five Hollywood movies proves the effectiveness of our proposed scheme in terms of user satisfaction. Furthermore, the objective evaluation verifies the superiority of the proposed scheme over state-of-the-art movie summarization methods.

scholarly journals Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Sharmeen M. Saleem Abdullah ◽  
◽  
Adnan Mohsin Abdulazeez ◽  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Facial Expression ◽  
Facial Expressions ◽  
Video Game ◽  
Facial Expression Recognition ◽  
Emotional Processing ◽  
Convolution Neural Network ◽  
Data Sets ◽  
Expression Recognition

Facial emotional processing is one of the most important activities in effective calculations, engagement with people and computers, machine vision, video game testing, and consumer research. Facial expressions are a form of nonverbal communication, as they reveal a person's inner feelings and emotions. Extensive attention to Facial Expression Recognition (FER) has recently been received as facial expressions are considered. As the fastest communication medium of any kind of information. Facial expression recognition gives a better understanding of a person's thoughts or views and analyzes them with the currently trending deep learning methods. Accuracy rate sharply compared to traditional state-of-the-art systems. This article provides a brief overview of the different FER fields of application and publicly accessible databases used in FER and studies the latest and current reviews in FER using Convolution Neural Network (CNN) algorithms. Finally, it is observed that everyone reached good results, especially in terms of accuracy, with different rates, and using different data sets, which impacts the results.

scholarly journals Face Recognition and Emotion Detection

2021 ◽  
Vol 9 (VI) ◽  
pp. 4775-4777
Author(s):  
V. J Chaudhari
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Face Recognition ◽  
Facial Expression ◽  
Video Game ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Emotion Detection ◽  
The People ◽  
Game Developers

This Face recognition and facial emotion detection is new era of technology. It’s also indirectly defining the level of growth in intelligence, security and copying human emotional behaviour. It is mainly used in market research and testing. Many companies require a good and accurate testing method which contributes to their development by providing the necessary insights and drawing the accurate conclusions. Facial expression recognition technology can be developed through various methods. This technology can be developed by using the deep learning with the convolutional neural network or with inbuilt libraries like deepface. The main objective here is to classify each face based on the emotions shown into seven categories which include Anger, Disgust, Fear, Happiness, Sadness, Surprise and Neutrality. The main objective here in this project is, to read the facial expressions of the people and displaying them the product which helps in determining their interest in it. Facial expression recognition technology can also be used in video game testing. During the video game testing, certain users are asked to play the game for a specified period and their expressions, and their behavior are monitored and analyzed. The game developers usually use the facial expression recognition and get the required insights and draw the conclusions and provide their feedback in the making of the final product. In this project, deep learning with the convolutional neural networks (CNN) approach is used. Neural networks need to be trained with large amounts of data and have a higher computational power [8-11]. It takes more time to train the model.[1]

Sign in / Sign up

Export Citation Format

Share Document