Emotion Recognition

High-resolution thermal infrared face database with extensive manual annotations, introduced by Kopaczka et al, 2018. Useful for training algoeithms for image processing tasks as well as facial expression recognition. The full database itself, all annotations and the complete source code are freely available from the authors for research purposes at https://github.com/marcinkopaczka/thermalfaceproject. Please cite following papers for the dataset: [1] M. Kopaczka, R. Kolk and D. Merhof, "A fully annotated thermal face database and its application for thermal facial expression recognition," 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2018, pp. 1-6, doi: 10.1109/I2MTC.2018.8409768. [2] Kopaczka, M., Kolk, R., Schock, J., Burkhard, F., & Merhof, D. (2018). A thermal infrared face database with facial landmarks and emotion labels. IEEE Transactions on Instrumentation and Measurement, 68(5), 1389-1401.

The Varied Emotion in Syntactically Uniform Speech (VESUS) repository is a lexically controlled database collected by the NSA lab. Here, actors read a semantically neutral script of words, phrases, and sentences with different emotional inflections. VESUS contains 252 distinct phrases, each read by 10 actors in 5 emotional states (neutral, angry, happy, sad, fearful). Source: https://engineering.jhu.edu/nsa/vesus/

This research introduces the dataset that we created to test voice emotional recognition models with Vietnamese data. The data set is the result of research, testing, and filtering 250 emotional segments from movie, movie series and live show divided equally for 5 basic emotional states of humans: “anger, happiness, sadness, neutral and anxiety”, VNEMOS contains approximately 30 minutes long. This dataset brings a balanced and diverse emotional set to study emotional recognition issues related to understanding human mood and emotions. Through evaluation Our dataset achieved a 89% accuracy, showcasing the adeptness at capturing the essence of positive emotions and robust learning capabilities in real-world scenarios. By clarifying these nuances. Our research will contribute much to future work on emotion recognition and signal processing in Vietnamese and data using human psychological science. Detailed information of the dataset is in the following link: “bit.ly/VNEMOS”.

Existing databases usually record posed or induced human behavior in individual or dyadic settings with biased annotations in which a basic emotional class label or a Valence–Arousal pair value represents the emotional states. To address this need,We created the “Werewolf-XL” database, which contains a total of 987 h of spontaneous audio-visual recordings of 129 subjects in a group interaction of nine individuals playing a conversational role-playing game called Werewolf. We provide 133,461 individual utterance-level video clips with internal (18 non-prototypical emotional categories) and external (pleasure, arousal, and dominance) emotion annotations including 14,635 samples from speaking players. Besides, the results of annotation agreement analysis show fair reliability and validity. Furthermore, we provided extensive benchmarks of unimodal and multimodal emotional recognition results. The database (the video clips, annotations, features and protocols) is made publicly available, which provides great opportunities for researchers in affective computing and beyond.

YM2413-MDB is an 80s FM video game music dataset with multi-label emotion annotations. It includes 669 audio and MIDI files of music from Sega and MSX PC games in the 80s using YM2413, a programmable sound generator based on FM. The collected game music is arranged with a subset of 15 monophonic instruments and one drum instrument. Source: Image Source: https://arxiv.org/pdf/2211.07131v1.pdf

Affective Text (Test Corpus of SemEval 2007) by Carlo Strapparava & Rada Mihalcea. Source: Affective Text

The volumetric representation of human interactions is one of the fundamental domains in the development of immersive media productions and telecommunication applications. Particularly in the context of the rapid advancement of Extended Reality (XR) applications, this volumetric data has proven to be an essential technology for future XR elaboration. In this work, we present a new multimodal database to help advance the development of immersive technologies. Our proposed database provides ethically compliant and diverse volumetric data, in particular 27 participants displaying posed facial expressions and subtle body movements while speaking, plus 11 participants wearing head-mounted displays (HMDs). The recording system consists of a volumetric capture (VoCap) studio, including 31 synchronized modules with 62 RGB cameras and 31 depth cameras. In addition to textured meshes, point clouds, and multi-view RGB-D data, we use one Lytro Illum camera for providing light field (LF) data simultaneously. Finally, we also provide an evaluation of our dataset employment with regard to the tasks of facial expression classification, HMDs removal, and point cloud reconstruction. The dataset can be helpful in the evaluation and performance testing of various XR algorithms, including but not limited to facial expression recognition and reconstruction, facial reenactment, and volumetric video. HEADSET and its all associated raw data and license agreement will be publicly available for research purposes.

This dataset consists of 600+ items of faces with different emotions and mixed races that are ready to use for optimizing the accuracy of computer vision models. Human age range from 20 to 60 years old, balance in gender, no occlusion, with head direction (<45 degree up-down and left-right). All of the contents is sourced from PIXTA's stock library of 100M+ Asian-featured images and videos. PIXTA is the largest platform of visual materials in the Asia Pacific region offering fully-managed services, high quality contents and data, and powerful tools for businesses & organizations to enable their creative and machine learning projects. For more details, please refer to the link: https://www.pixta.ai/ Or send your inquiries to contact@pixta.ai

We designed an emotional speech database that can be used for emotion recognition as well as recognition and synthesis of speech with various emotions. The database was designed by compiling tweets acquired from Twitter and selecting emotion- dependent tweets considering phonetic and prosodic balance. We classified gathered tweets into four emotions: joy, anger, sadness and neutral, and then selected 50 sentences from sentences of each emotion based on the entropy-based algorithm. We compared the selected sentence sets with randomly selected sentence sets from aspects of phonetic and prosodic balance and sentence length, and confirmed that the sets selected by the algorithm were more balanced. Next, we recorded emotional speech based on the selected sentences. Then, we evaluated the speech from the viewpoint of emotional recognition and emotional speech recognition.

Welcome to L-SVD L-SVD is an extensive and rigorously curated video dataset aimed at transforming the field of emotion recognition. This dataset features more than 20,000 short video clips, each carefully annotated to represent a range of human emotions. L-SVD stands at the intersection of Cognitive Science, Psychology, Computer Science, and Medical Science, providing a unique tool for both research and application in these fields. ### Why L-SVD? Inspired by the revolutionary ImageNet, L-SVD aspires to be a benchmark in the emotional AI space, offering meticulously labeled and consistently processed video data to the global research community. - Rich Emotional Annotations: Our dataset encompasses a spectrum of eight emotions—Anger, Contempt, Disgust, Enjoyment, Fear, Sadness, Surprise, and Neutral—each annotated with precision. - Uniform Video Quality: We ensure uniform hue, contrast, and brightness across all videos to provide a standard quality baseline for algorithm development. - Exponential Growth and Community Driven: L-SVD is rapidly expanding, with new contributions regularly enhancing the depth and breadth of the dataset. ### Dataset Features - Detailed Emotional Range: L-SVD covers a broad array of emotional states, meticulously labeled for accurate recognition. - Pre-Processed for Excellence: All clips are pre-processed to ensure high fidelity and uniformity, setting a high standard for training data quality. - Inviting Global Contributions: We encourage contributions from researchers and practitioners around the world to help grow and diversify the dataset.

A physiological signal dataset with multiple physiological signals collected from 30 participants. The dataset consists of EEG, EDA, BVP, and temperature information with annotations in both categorical and dimensional views along with the individual personality traits of the participants for the study of emotions in presence of individual personality differences. The PhyMER dataset consists of the recorded physiological signals, participants' personality information, and emotion annotations in terms of Arousal, Valence, and seven basic emotions. Modality: Electroencephalogram(EEG), Electrodermal Activity(EDA), Blood Pulse Volume(BVP), Heart Rate(HR), Peripheral Skin Temperature(TEMP), Personality Traits Labels: Arousal and valence(1-9) Seven Basic Emotions(Anger, Disgust, Fear, Happy, Neutral, Sad, Surprise)

The Storytelling Video Dataset is a high-quality, human-reviewed multimodal dataset featuring over 700 full-body video recordings of native Russian speakers. Each video is 10+ minutes long and includes synchronized speech, facial expressions, gestures, and emotional variation. The dataset is ideal for research and development in: 🗣️ Speech-to-text & voice modeling (Russian) 😃 Emotion & gesture recognition 🤖 Multimodal learning & LLM alignment 🧍 Avatar generation & digital human training Key Features: Full-body framing (waist-up or knee-up) Clear facial expressions and gestures High-quality speech audio in Russian Natural storytelling with emotional diversity Preview (10 Participants): We’ve prepared a short compilation of 10 different speakers from the dataset. Each clip is taken from a unique 10-minute unscripted video, featuring full-body framing, gestures, and emotional speech. 📺 Watch the sample: https://drive.google.com/drive/folders/1QtC2il-Qb62nZNJlOtG8WvOf-SSkSGM-?usp=drive_link – Video Preview Dataset Pages: 🐙 GitHub: github.com/MaratDV 📊 DataHub: datahub.io/@MaratDV/storytelling-video-dataset email chinzad@gmail.com or telegram @Marat_DV