Book ChapterDOI
Artificially Intelligent Game Framework Based on Facial Expression Recognition
Itisha Patidar,Karan Sanjay Modh,Chiranjoy Chattopadhyay +2 more
- pp 312-321
Reads0
Chats0
TLDR
In this article, an artificially intelligent game framework with smart features based on automatic facial expression recognition and adaptive game features was presented, where the gamer's emotions were recognized at run-time during gameplay using Deep Convolutional Neural Networks (CNN), and the game was adapted accordingly to the emotional condition.Abstract:
During gameplay, a player experiences emotional turmoil. In most of the cases, these emotions directly reflect the outcome of the game. Adapting game features based on players’ emotions necessitates a way to detect the current emotional state. Researchers in the area of “video game user research” has studied biometric data as a way to address the diverse characteristics of players, their individual preferences, gameplay expertise, and experiences. Identification of the player’s current state is fundamental for designing a game, which interacts with the player adaptively. In this paper, we present an artificially intelligent game framework with smart features based on automatic facial expression recognition and adaptive game features based on the gamer’s emotion. The gamer’s emotions are recognized at run-time during gameplay using Deep Convolutional Neural Networks (CNN), and the game is adapted accordingly to the emotional condition. Once identified, these features directly modify critical parameters of the underlying game engine to make the game more exciting and challenging.read more
References
More filters
Journal ArticleDOI
Robust Real-Time Face Detection
Paul A. Viola,Michael Jones +1 more
TL;DR: In this paper, a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates is described. But the detection performance is limited to 15 frames per second.
Proceedings ArticleDOI
Robust real-time face detection
Paul A. Viola,Michael Jones +1 more
TL;DR: A new image representation called the “Integral Image” is introduced which allows the features used by the detector to be computed very quickly and a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions.
Proceedings ArticleDOI
Going deeper in facial expression recognition using deep neural networks
TL;DR: A deep neural network architecture to address the FER problem across multiple well-known standard face datasets is proposed, comparable to or better than the state-of-the-art methods and better than traditional convolutional neural networks in both accuracy and training time.
Journal ArticleDOI
Facial expression recognition with Convolutional Neural Networks
TL;DR: A simple solution for facial expression recognition that uses a combination of Convolutional Neural Network and specific image pre-processing steps to extract only expression specific features from a face image and explore the presentation order of the samples during training.
Proceedings ArticleDOI
Boosted local structured HOG-LBP for object localization
TL;DR: This paper proposes a boosted Local Structured HOG-LBP based object detector to capture the object's local structure, and develop the descriptors from shape and texture information, respectively, and presents a boosted feature selection and fusion scheme for part based object detectors.