Ringer et al., 2019 - Google Patents
Multimodal joint emotion and game context recognition in league of legends livestreamsRinger et al., 2019
View PDF- Document ID
- 9278594743353407143
- Author
- Ringer C
- Walker J
- Nicolaou M
- Publication year
- Publication venue
- 2019 IEEE Conference on Games (CoG)
External Links
Snippet
Video game streaming provides the viewer with a rich set of audio-visual data, conveying information both with regards to the game itself, through game footage and audio, as well as the streamer's emotional state and behaviour via webcam footage and audio. Analysing …
- 230000004927 fusion 0 abstract description 71
Classifications
-
- 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
- G06K9/00268—Feature extraction; Face representation
- G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
-
- 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
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- 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
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- 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
- G06K9/00362—Recognising human body or animal bodies, e.g. vehicle occupant, pedestrian; Recognising body parts, e.g. hand
- G06K9/00369—Recognition of whole body, e.g. static pedestrian or occupant recognition
-
- 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
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Duarte et al. | WAV2PIX: Speech-conditioned Face Generation using Generative Adversarial Networks. | |
Sepas-Moghaddam et al. | View-invariant gait recognition with attentive recurrent learning of partial representations | |
Ringer et al. | Multimodal joint emotion and game context recognition in league of legends livestreams | |
Kuehne et al. | HMDB: a large video database for human motion recognition | |
Sokolova et al. | Gait recognition based on convolutional neural networks | |
Makantasis et al. | From pixels to affect: A study on games and player experience | |
Makantasis et al. | The pixels and sounds of emotion: General-purpose representations of arousal in games | |
Fernando et al. | Memory augmented deep generative models for forecasting the next shot location in tennis | |
Mousavi et al. | Learning to predict where to look in interactive environments using deep recurrent q-learning | |
Ahad et al. | Action dataset—A survey | |
Hou et al. | Spatially and temporally structured global to local aggregation of dynamic depth information for action recognition | |
Cartas et al. | Activities of daily living monitoring via a wearable camera: Toward real-world applications | |
Pilz et al. | Walk this way: Approaching bodies can influence the processing of faces | |
KR102702069B1 (en) | Method of controlling sports activity classification learning apparatus, computer readable medium and apparatus for performing the method | |
Li et al. | Towards an “in-the-wild” emotion dataset using a game-based framework | |
Torpey et al. | Human action recognition using local two-stream convolution neural network features and support vector machines | |
Martin et al. | 3D Convolutional Networks for Action Recognition: Application to Sport Gesture Recognition | |
Monteiro et al. | Evaluating the feasibility of deep learning for action recognition in small datasets | |
Chiu et al. | Smoking action recognition based on spatial-temporal convolutional neural networks | |
Kohn et al. | Event-driven body motion analysis for real-time gesture recognition | |
Khedkar et al. | Exploiting spatiotemporal inconsistencies to detect deepfake videos in the wild | |
Apon et al. | Action recognition using transfer learning and majority voting for csgo | |
Cai et al. | Video saliency prediction for First-Person View UAV videos: Dataset and benchmark | |
Mahajan et al. | Depth and skeleton based view-invariant human action recognition | |
Nicolaou et al. | Streaming behaviour: Live streaming as a paradigm for multi-view analysis of emotional and social signals |