Gavrilescu et al., 2019 - Google Patents
Predicting depression, anxiety, and stress levels from videos using the facial action coding systemGavrilescu et al., 2019
View HTML- Document ID
- 1672847846162394253
- Author
- Gavrilescu M
- Vizireanu N
- Publication year
- Publication venue
- Sensors
External Links
Snippet
We present the first study in the literature that has aimed to determine Depression Anxiety Stress Scale (DASS) levels by analyzing facial expressions using Facial Action Coding System (FACS) by means of a unique noninvasive architecture on three layers designed to …
- 230000001815 facial 0 title abstract description 99
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
-
- 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
- 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
- G06Q50/22—Health care, e.g. hospitals; Social work
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
-
- 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
-
- 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
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
-
- 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
- G06Q50/01—Social networking
-
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Gavrilescu et al. | Predicting depression, anxiety, and stress levels from videos using the facial action coding system | |
Samadiani et al. | A review on automatic facial expression recognition systems assisted by multimodal sensor data | |
Ed-Doughmi et al. | Real-time system for driver fatigue detection based on a recurrent neuronal network | |
Leo et al. | Computational assessment of facial expression production in ASD children | |
Sepúlveda et al. | Emotion recognition from ECG signals using wavelet scattering and machine learning | |
Kim et al. | EEG-based emotion classification using long short-term memory network with attention mechanism | |
Oh et al. | Drer: Deep learning–based driver’s real emotion recognizer | |
Milanés-Hermosilla et al. | Monte Carlo dropout for uncertainty estimation and motor imagery classification | |
Tan et al. | Fusionsense: Emotion classification using feature fusion of multimodal data and deep learning in a brain-inspired spiking neural network | |
Arevalillo-Herráez et al. | Combining inter-subject modeling with a subject-based data transformation to improve affect recognition from EEG signals | |
Liu et al. | Emotion recognition from single-trial EEG based on kernel Fisher's emotion pattern and imbalanced quasiconformal kernel support vector machine | |
Zaman et al. | Driver emotions recognition based on improved faster R-CNN and neural architectural search network | |
Khan et al. | A multi-task framework for facial attributes classification through end-to-end face parsing and deep convolutional neural networks | |
Ma et al. | Learning better representations for audio-visual emotion recognition with common information | |
Anber et al. | A hybrid driver fatigue and distraction detection model using AlexNet based on facial features | |
Wang et al. | EEG-based emotion recognition using a 2D CNN with different kernels | |
Liu et al. | Heart rate measurement based on 3d central difference convolution with attention mechanism | |
El Kerdawy et al. | The automatic detection of cognition using eeg and facial expressions | |
Zheng et al. | Detecting Dementia from Face-Related Features with Automated Computational Methods | |
Miranda et al. | Fear recognition for women using a reduced set of physiological signals | |
Kyamakya et al. | Emotion and stress recognition related sensors and machine learning technologies | |
Yuvaraj et al. | Emotion recognition from spatio-temporal representation of EEG signals via 3D-CNN with ensemble learning techniques | |
Aguiñaga et al. | Emotion recognition by correlating facial expressions and EEG analysis | |
Wierciński et al. | Emotion recognition from physiological channels using graph neural network | |
Lee et al. | Video-based contactless heart-rate detection and counting via joint blind source separation with adaptive noise canceller |