scholarly journals CorrNet: Fine-Grained Emotion Recognition for Video Watching Using Wearable Physiological Sensors

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 52
Author(s):  
Tianyi Zhang ◽  
Abdallah El Ali ◽  
Chen Wang ◽  
Alan Hanjalic ◽  
Pablo Cesar
Keyword(s):  
Emotion Recognition ◽  
Electrodermal Activity ◽  
Short Form ◽  
Recognition Performance ◽  
Binary Classification ◽  
Wearable Sensors ◽  
Recognition Algorithm ◽  
Fine Grained ◽  
Physiological Sensors ◽  
Valence And Arousal

Recognizing user emotions while they watch short-form videos anytime and anywhere is essential for facilitating video content customization and personalization. However, most works either classify a single emotion per video stimuli, or are restricted to static, desktop environments. To address this, we propose a correlation-based emotion recognition algorithm (CorrNet) to recognize the valence and arousal (V-A) of each instance (fine-grained segment of signals) using only wearable, physiological signals (e.g., electrodermal activity, heart rate). CorrNet takes advantage of features both inside each instance (intra-modality features) and between different instances for the same video stimuli (correlation-based features). We first test our approach on an indoor-desktop affect dataset (CASE), and thereafter on an outdoor-mobile affect dataset (MERCA) which we collected using a smart wristband and wearable eyetracker. Results show that for subject-independent binary classification (high-low), CorrNet yields promising recognition accuracies: 76.37% and 74.03% for V-A on CASE, and 70.29% and 68.15% for V-A on MERCA. Our findings show: (1) instance segment lengths between 1–4 s result in highest recognition accuracies (2) accuracies between laboratory-grade and wearable sensors are comparable, even under low sampling rates (≤64 Hz) (3) large amounts of neutral V-A labels, an artifact of continuous affect annotation, result in varied recognition performance.

scholarly journals Fast Emotion Recognition Based on Single Pulse PPG Signal with Convolutional Neural Network

2019 ◽  
Vol 9 (16) ◽  
pp. 3355 ◽  
Author(s):  
Min Seop Lee ◽  
Yun Kyu Lee ◽  
Dong Sung Pae ◽  
Myo Taeg Lim ◽  
Dong Won Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Emotion Recognition ◽  
Binary Classification ◽  
Single Pulse ◽  
Signal Pulse ◽  
Short Term ◽  
Signal Features ◽  
The Neural Network ◽  
Valence And Arousal

Physiological signals contain considerable information regarding emotions. This paper investigated the ability of photoplethysmogram (PPG) signals to recognize emotion, adopting a two-dimensional emotion model based on valence and arousal to represent human feelings. The main purpose was to recognize short term emotion using a single PPG signal pulse. We used a one-dimensional convolutional neural network (1D CNN) to extract PPG signal features to classify the valence and arousal. We split the PPG signal into a single 1.1 s pulse and normalized it for input to the neural network based on the personal maximum and minimum values. We chose the dataset for emotion analysis using physiological (DEAP) signals for the experiment and tested the 1D CNN as a binary classification (high or low valence and arousal), achieving the short-term emotion recognition of 1.1 s with 75.3% and 76.2% valence and arousal accuracies, respectively, on the DEAP data.

A Kind of ECC - KNN Classifier's Vehicle Identification Algorithm

2013 ◽  
Vol 347-350 ◽  
pp. 3724-3727
Author(s):  
Lin Tao Lü ◽  
Huan Gao ◽  
Yu Xiang Yang
Keyword(s):  
Recognition Performance ◽  
Binary Classification ◽  
Recognition Algorithm ◽  
Identification Algorithm ◽  
Classification Problems ◽  
Vehicle Recognition ◽  
Vehicle Identification ◽  
Image Characteristics ◽  
Multi Class Classification

this article presents an improved classifier vehicle identification algorithm to improve the efficiency of the existing vehicle recognition algorithm. First, using edge orientation histograms to extract image characteristics, then, Error correction coding is applied to the classification of classifier, the multi-class classification problems turned into multiple binary classification problems. A large number of experimental analysis shows that the improved vehicle identification algorithm has good recognition performance and robustness. Therefore, the algorithm which this article used has high theoretical and practical value.

scholarly journals Facial emotion recognition using Min-Max similarity classifier

2017 ◽  
Author(s):  
Olga Krestinskaya ◽  
Alex Pappachen James
Keyword(s):  
Emotion Recognition ◽  
Template Matching ◽  
Intelligent Systems ◽  
Nearest Neighbor ◽  
Recognition Performance ◽  
Recognition Algorithm ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Recognition Of Facial Expressions ◽  
Matching Techniques

Recognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in recent years, the effective and computationally simple feature selection and classification technique for emotion recognition is still an open problem. In this paper, we propose an efficient and straightforward facial emotion recognition algorithm to reduce the problem of inter-class pixel mismatch during classification. The proposed method includes the application of pixel normalization to remove intensity offsets followed-up with a Min-Max metric in a nearest neighbor classifier that is capable of suppressing feature outliers. The results indicate an improvement of recognition performance from 92.85% to 98.57% for the proposed Min-Max classification method when tested on JAFFE database. The proposed emotion recognition technique outperforms the existing template matching methods.

scholarly journals Electroencephalogram Emotion Recognition Based on 3D Feature Fusion and Convolutional Autoencoder

2021 ◽  
Vol 15 ◽  
Author(s):  
Yanling An ◽  
Shaohai Hu ◽  
Xiaoying Duan ◽  
Ling Zhao ◽  
Caiyun Xie ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Spatial Information ◽  
Feature Fusion ◽  
Recognition Accuracy ◽  
Recognition Algorithm ◽  
Eeg Signals ◽  
Key Technologies ◽  
Convolutional Autoencoder ◽  
Valence And Arousal ◽  
Electroencephalogram Eeg

As one of the key technologies of emotion computing, emotion recognition has received great attention. Electroencephalogram (EEG) signals are spontaneous and difficult to camouflage, so they are used for emotion recognition in academic and industrial circles. In order to overcome the disadvantage that traditional machine learning based emotion recognition technology relies too much on a manual feature extraction, we propose an EEG emotion recognition algorithm based on 3D feature fusion and convolutional autoencoder (CAE). First, the differential entropy (DE) features of different frequency bands of EEG signals are fused to construct the 3D features of EEG signals, which retain the spatial information between channels. Then, the constructed 3D features are input into the CAE constructed in this paper for emotion recognition. In this paper, many experiments are carried out on the open DEAP dataset, and the recognition accuracy of valence and arousal dimensions are 89.49 and 90.76%, respectively. Therefore, the proposed method is suitable for emotion recognition tasks.

scholarly journals Facial emotion recognition using Min-Max similarity classifier

2017 ◽  
Author(s):  
Olga Krestinskaya ◽  
Alex Pappachen James
Keyword(s):  
Emotion Recognition ◽  
Template Matching ◽  
Intelligent Systems ◽  
Nearest Neighbor ◽  
Recognition Performance ◽  
Recognition Algorithm ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Recognition Of Facial Expressions ◽  
Matching Techniques

Recognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in recent years, the effective and computationally simple feature selection and classification technique for emotion recognition is still an open problem. In this paper, we propose an efficient and straightforward facial emotion recognition algorithm to reduce the problem of inter-class pixel mismatch during classification. The proposed method includes the application of pixel normalization to remove intensity offsets followed-up with a Min-Max metric in a nearest neighbor classifier that is capable of suppressing feature outliers. The results indicate an improvement of recognition performance from 92.85% to 98.57% for the proposed Min-Max classification method when tested on JAFFE database. The proposed emotion recognition technique outperforms the existing template matching methods.

Maternal Perception of Infants’ Expressions of Emotion

2010 ◽  
Vol 24 (3) ◽  
pp. 173-185 ◽  
Author(s):  
Martin Krippl ◽  
Stephanie Ast-Scheitenberger ◽  
Ina Bovenschen ◽  
Gottfried Spangler
Keyword(s):  
Response Pattern ◽  
Electrodermal Activity ◽  
Startle Amplitude ◽  
Caregiving System ◽  
Parental Caregiving ◽  
Opponent Process ◽  
Opponent Process Theory ◽  
Valence And Arousal ◽  
Methodological Considerations

In light of Lang’s differentiation of the aversive and the approach system – and assumptions stemming from attachment theory – this study investigates the role of the approach or caregiving system for processing infant emotional stimuli by comparing IAPS pictures, infant pictures, and videos. IAPS pictures, infant pictures, and infant videos of positive, neutral, or negative content were presented to 69 mothers, accompanied by randomized startle probes. The assessment of emotional responses included subjective ratings of valence and arousal, corrugator activity, the startle amplitude, and electrodermal activity. In line with Lang’s original conception, the typical startle response pattern was found for IAPS pictures, whereas no startle modulation was observed for infant pictures. Moreover, the startle amplitudes during negative video scenes depicting crying infants were reduced. The results are discussed with respect to several theoretical and methodological considerations, including Lang’s theory, emotion regulation, opponent process theory, and the parental caregiving system.

Short Report: Speeded Reasoning Moderates the Inverse Relationship Between Autistic-Like Traits and Emotion Recognition

2019 ◽  
Author(s):  
Alex Bertrams ◽  
Katja Schlegel
Keyword(s):  
Emotion Recognition ◽  
Inverse Relationship ◽  
Time Pressure ◽  
Recognition Performance ◽  
Autism Spectrum ◽  
Autism Spectrum Quotient ◽  
Short Report ◽  
Reasoning Ability ◽  
Reasoning Test ◽  
The Mind

People high in autistic-like traits have been found to have difficulties with recognizing emotions from nonverbal expressions. However, findings on the autism—emotion recognition relationship are inconsistent. In the present study, we investigated whether speeded reasoning ability (reasoning performance under time pressure) moderates the inverse relationship between autistic-like traits and emotion recognition performance. We expected the negative correlation between autistic-like traits and emotion recognition to be less strong when speeded reasoning ability was high. MTurkers (N = 217) completed the ten item version of the Autism Spectrum Quotient (AQ-10), two emotion recognition tests using videos with sound (Geneva Emotion Recognition Test, GERT-S) and pictures (Reading the Mind in the Eyes Test, RMET), and Baddeley's Grammatical Reasoning test to measure speeded reasoning. As expected, the higher the ability in speeded reasoning, the less were higher autistic-like traits related to lower emotion recognition performance. These results suggest that a high ability in making quick mental inferences may (partly) compensate for difficulties with intuitive emotion recognition related to autistic-like traits.

scholarly journals Advancing Stress Detection Methodology with Deep Learning Techniques Targeting UX Evaluation in AAL Scenarios: Applying Embeddings for Categorical Variables

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1550
Author(s):  
Alexandros Liapis ◽  
Evanthia Faliagka ◽  
Christos P. Antonopoulos ◽  
Georgios Keramidas ◽  
Nikolaos Voros
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
User Experience ◽  
Electrodermal Activity ◽  
Binary Classification ◽  
Research Question ◽  
Classification Problem ◽  
Categorical Variables ◽  
Stress Detection ◽  
Software Failures

Physiological measurements have been widely used by researchers and practitioners in order to address the stress detection challenge. So far, various datasets for stress detection have been recorded and are available to the research community for testing and benchmarking. The majority of the stress-related available datasets have been recorded while users were exposed to intense stressors, such as songs, movie clips, major hardware/software failures, image datasets, and gaming scenarios. However, it remains an open research question if such datasets can be used for creating models that will effectively detect stress in different contexts. This paper investigates the performance of the publicly available physiological dataset named WESAD (wearable stress and affect detection) in the context of user experience (UX) evaluation. More specifically, electrodermal activity (EDA) and skin temperature (ST) signals from WESAD were used in order to train three traditional machine learning classifiers and a simple feed forward deep learning artificial neural network combining continues variables and entity embeddings. Regarding the binary classification problem (stress vs. no stress), high accuracy (up to 97.4%), for both training approaches (deep-learning, machine learning), was achieved. Regarding the stress detection effectiveness of the created models in another context, such as user experience (UX) evaluation, the results were quite impressive. More specifically, the deep-learning model achieved a rather high agreement when a user-annotated dataset was used for validation.

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