Tsangouri, 2016 - Google Patents
Towards an" In-the-Wild" Emotion Dataset Using a Game-based FrameworkTsangouri, 2016
View HTML- Document ID
- 16523619779845246045
- Author
- Tsangouri C
- Publication year
- Publication venue
- arXiv (Cornell University)
External Links
Snippet
In order to create an" in-the-wild" dataset of facial emotions with large number of balanced samples, this paper proposes a game-based data collection framework. The framework mainly include three components: a game engine, a game interface, and a data collection …
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30781—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F17/30784—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/017—Gesture based interaction, e.g. based on a set of recognized hand gestures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
- G06K9/00288—Classification, e.g. identification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00335—Recognising movements or behaviour, e.g. recognition of gestures, dynamic facial expressions; Lip-reading
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
-
- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B5/00—Electrically-operated educational appliances
- G09B5/02—Electrically-operated educational appliances with visual presentation of the material to be studied, e.g. using film strip
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Fothergill et al. | Instructing people for training gestural interactive systems | |
| US20240398303A1 (en) | Systems and methods for automated recognition of bodily expression of emotion | |
| Zhu et al. | Multi-rate attention based gru model for engagement prediction | |
| US11007445B2 (en) | Techniques for curation of video game clips | |
| Xu et al. | Msr-vtt: A large video description dataset for bridging video and language | |
| CN115951786B (en) | A method for creating a creative social game with multiple endings using AIGC technology | |
| Lan et al. | Eyesyn: Psychology-inspired eye movement synthesis for gaze-based activity recognition | |
| Ghosh et al. | Predicting group cohesiveness in images | |
| Gupta et al. | DAISEE: dataset for affective states in e-learning environments | |
| Li et al. | A recursive framework for expression recognition: from web images to deep models to game dataset | |
| Petrova et al. | Group-level emotion recognition using a unimodal privacy-safe non-individual approach | |
| Li et al. | Towards an “in-the-wild” emotion dataset using a game-based framework | |
| Wei et al. | The science and detection of tilting | |
| Zhang et al. | Werewolf-xl: A database for identifying spontaneous affect in large competitive group interactions | |
| Kim et al. | Bubbleu: Exploring augmented reality game design with uncertain AI-based interaction | |
| WO2024253768A2 (en) | Ai highlight detection trained on shared video | |
| Pinitas et al. | Varying the context to advance affect modelling: A study on game engagement prediction | |
| Ortmann et al. | EmojiHeroVR: a study on facial expression recognition under partial occlusion from head-mounted displays | |
| Gao et al. | Identity-free artificial emotional intelligence via micro-gesture understanding | |
| Wang et al. | Hidden Markov Model‐Based Video Recognition for Sports | |
| Wang | Using machine learning algorithms to recognize shuttlecock movements | |
| Li et al. | Towards an | |
| Patel et al. | AI-Driven Emotion-Aware Adaptive Systems for Enhancing Real-Time User Engagement | |
| Tsangouri | Towards an" In-the-Wild" Emotion Dataset Using a Game-based Framework | |
| Mandıra et al. | Spatiotemporal and multimodal analysis of personality traits |