The dataset for this project is characterised by photos of individual human emotion expression and these photos are taken with the help of both digital camera and a mobile phone camera from different angles, posture, background, light exposure, and distances. This task might look and sound very easy but there were some challenges encountered along the process which are reviewed below: 1) People constraint One of the major challenges faced during this project is getting people to participate in the image capturing process as school was on vacation, and other individuals gotten around the environment were not willing to let their images be captured for personal and security reasons even after explaining the notion behind the project which is mainly for academic research purposes. Due to this challenge, we resorted to capturing the images of the researcher and just a few other willing individuals. 2) Time constraint As with all deep learning projects, the more data available the more accuracy and less error the result will produce. At the initial stage of the project, it was agreed to have 10 emotional expression photos each of at least 50 persons and we can increase the number of photos for more accurate results but due to the constraint in time of this project an agreement was later made to just capture the researcher and a few other people that are willing and available. These photos were taken for just two types of human emotion expression that is, “happy” and “sad” faces due to time constraint too. To expand our work further on this project (as future works and recommendations), photos of other facial expression such as anger, contempt, disgust, fright, and surprise can be included if time permits. 3) The approved facial emotions capture. It was agreed to capture as many angles and posture of just two facial emotions for this project with at least 10 images emotional expression per individual, but due to time and people constraints few persons were captured with as many postures as possible for this project which is stated below: Ø Happy faces: 65 images Ø Sad faces: 62 images There are many other types of facial emotions and again to expand our project in the future, we can include all the other types of the facial emotions if time permits, and people are readily available. 4) Expand Further. This project can be improved furthermore with so many abilities, again due to the limitation of time given to this project, these improvements can be implemented later as future works. In simple words, this project is to detect/predict real-time human emotion which involves creating a model that can detect the percentage confidence of any happy or sad facial image. The higher the percentage confidence the more accurate the facial fed into the model. 5) Other Questions Can the model be reproducible? the supposed response to this question should be YES. If and only if the model will be fed with the proper data (images) such as images of other types of emotional expression.

Animate/inanimate face recognition task was administered using the Presentation program (Neurobehavioral Systems). Face stimuli were obtained from Looser & Wheatley (2010) who morphed twenty inanimate faces with well-matched photographs of human faces using FantaMorph software (Version 3; Abrosoft Co., Beijing, China). The resulting image sets were the product of linear interpolation between the two (human/mannequin) original images. This kept the increments of physical change consistent across the morphing continuum, which consisted of 5 ‘human’ faces (1-5, containing 60-100% human aspects) and 5 ‘mannequin’ faces (7-11, containing 60-100% mannequin aspects) and a sixth, middle face that was considered a 50% human-50% mannequin version.This task consisted of the presentation of 9 blocks of faces presented on a different 15” laptop screen, placed at about eye level approximately 66 cm in front of participants. Each face stimulus block contained a sequence of 11 human-like faces morphed as previously described. This provided a total of 99 faces. When instructed to begin, participants saw a randomly selected face in the middle of the screen, (W: 10-12 cm; H: 11-15 cm) followed by a question as to whether the face was inanimate (1) or animate (2). Each trial lasted 3000ms (1000 ms face stimuli duration and 2000 ms response screen duration). Participants were instructed to relax and minimize movements since EEG was being recorded simultaneously.Presentation of faces was immediately followed by a question as to whether the face was inanimate (1) or animate (2). Reaction times (RTs) were collected for those responses. Outlier analysis of the data was performed in which RTs more than two standard deviations above or below the mean were removed. Participant responses were also analyzed by using polynomial regression fitting to compute the functions representing subjects’ responses. This was done by collecting and analyzing response data from individual subjects across the 9 face blocks (Cheetham & Jancke, 2013), each of which was shown to participants before and after stimulation. Each of the 9 face blocks contained 11 different morph images with a unique morph identifier, also known as a face continua. Response data for each face continua were aggregated and averaged, generating 9 functions displaying the response data prior to stimulation and 9 functions following stimulation. In pooling the 18 different polynomial curves, four cumulative functions were generated -- one for pre-STIM, post-STIM, pre-SHAM, and post-SHAM, thus allowing larger inferences on how participants responded to each morph image prior to and following the onset of stimulation to be drawn. Once the four cumulative functions were generated, the point of subjective equality (PSE) was calculated. This is the point at which a face was equally likely to be deemed animate or inanimate (following Looser & Wheatley, 2010). It was done by finding the polynomial function associated with the fitted curve and finding the x-coordinate corresponding to the point at which the function reached the 50% recognition point.

Summary

Facial expression is among the most natural methods for human beings to convey their emotional information in daily life. Although the neural mechanism of facial expression has been extensively studied employing lab-controlled images and a small number of lab-controlled video stimuli, how the human brain processes natural facial expressions still needs to be investigated. To our knowledge, this type of data specifically on large number of natural facial expression videos is currently missing. We describe here the natural Facial Expressions Dataset (NFED), a fMRI dataset including responses to 1,320 short (3-second) natural facial expression video clips. These video clips is annotated with three types of labels: emotion, gender, and ethnicity, along with accompanying metadata. We validate that the dataset has good quality within and across participants and, notably, can capture temporal and spatial stimuli features. NFED provides researchers with fMRI data for understanding of the visual processing of large number of natural facial expression videos.

Data Records

The data, which were structured following the BIDS format53, were accessible at https://openneuro.org/datasets/ds00504754. The “sub-

Stimulus. Distinct folders store the stimuli for distinct fMRI experiments: "stimuli/face-video", "stimuli/floc", and "stimuli/prf" (Fig. 2b). The category labels and metadata corresponding to video stimuli are stored in the "videos-stimuli_category_metadata.tsv”. The “videos-stimuli_description.json” file describes category and metadata information of video stimuli(Fig. 2b).

Raw MRI data. Each participant's folder is comprised of 11 session folders: “sub-

Volume data from pre-processing. The pre-processed volume-based fMRI data were in the folder named “pre-processed_volume_data/sub-

Surface data from pre-processing. The pre-processed surface-based data were stored in a file named “volumetosurface/sub-

FreeSurfer recon-all. The results of reconstructing the cortical surface were saved as “recon-all-FreeSurfer/sub-

Surface-based GLM analysis data. We have conducted GLMsingle on the data of the main experiment. There is a file named “sub--

Validation. The code of technical validation was saved in the “derivatives/validation/code” folder. The results of technical validation were saved in the “derivatives/validation/results” folder (Fig. 2h). “README.md” describes the detailed information of code and results.

Supplementary materials for the submitted article: Facial reactions to face representations in art.

Gender Detection & Classification - face recognition dataset

The dataset is created on the basis of Face Mask Detection dataset

Dataset Description:

The dataset comprises a collection of photos of people, organized into folders labeled "women" and "men." Each folder contains a significant number of images to facilitate training and testing of gender detection algorithms or models.

The dataset contains a variety of images capturing female and male individuals from diverse backgrounds, age groups, and ethnicities.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F1c4708f0b856f7889e3c0eea434fe8e2%2FFrame%2045%20(1).png?generation=1698764294000412&alt=media" alt="">

This labeled dataset can be utilized as training data for machine learning models, computer vision applications, and gender detection algorithms.

💴 For Commercial Usage: Full version of the dataset includes 376 000+ photos of people, leave a request on TrainingData to buy the dataset

Metadata for the full dataset:

• assignment_id - unique identifier of the media file
• worker_id - unique identifier of the person
• age - age of the person
• true_gender - gender of the person
• country - country of the person
• ethnicity - ethnicity of the person
• photo_1_extension, photo_2_extension, photo_3_extension, photo_4_extension - photo extensions in the dataset
• photo_1_resolution, photo_2_resolution, photo_3_extension, photo_4_resolution - photo resolution in the dataset

OTHER BIOMETRIC DATASETS:

• Anti Spoofing Real Dataset
• Antispoofing Replay Dataset
• Selfies, ID Images dataset (5591 sets of 15 files)
• Selfies and video dataset (4 052 sets)
• Dataset of bald people, 5000 images

💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/datasets to learn about the price and buy the dataset

Content

The dataset is split into train and test folders, each folder includes: - folders women and men - folders with images of people with the corresponding gender, - .csv file - contains information about the images and people in the dataset

File with the extension .csv

• file: link to access the file,
• gender: gender of a person in the photo (woman/man),
• split: classification on train and test

TrainingData provides high-quality data annotation tailored to your needs

keywords: biometric system, biometric system attacks, biometric dataset, face recognition database, face recognition dataset, face detection dataset, facial analysis, gender detection, supervised learning dataset, gender classification dataset, gender recognition dataset

Collection description

We introduce a new method for detecting differences in the latency of blood oxygenation level-dependent (BOLD) responses to brief events within the context of the General Linear Model. Using a first-order Taylor approximation in terms of the temporal derivative of a canonical hemodynamic response function, statistical parametric maps of differential latencies were estimated via the ratio of derivative to canonical parameter estimates. This method was applied to two example datasets: comparison of words versus nonwords in a lexical decision task and initial versus repeated presentations of faces in a fame-judgment task. Tests across subjects revealed both magnitude and latency differences within several brain regions. This approach offers a computationally efficient means of detecting BOLD latency differences over the whole brain. Precise characterization of the hemodynamic latency and its interpretation in terms of underlying neural differences remain problematic, however.

Subject species

homo sapiens

Modality

fMRI-BOLD

Analysis level

single-subject

Map type

Z

People tend to judge repeated information as more likely being true compared to new information. A key explanation for this phenomenon, called the illusory truth effect, is that repeated information can be processed more fluently, causing it to appear more familiar and trustworthy. To consider the function of time in investigating its underlying cognitive and affective mechanisms, our design comprised two retention intervals. 75 participants rated the truth of new and repeated statements 10 minutes as well as 1 week after first exposure while spontaneous facial expressions were assessed via electromyography. Our data demonstrate that repetition results in specific facial reactions indicating increased positive affect, reduced mental effort, and increased familiarity (i.e., relaxations of musculus corrugator supercilii and frontalis), and subsequently increases the probability of judging information as true. The results moreover highlight the relevance of time: both repetition-induced truth effect and EMG activities decrease significantly after a longer interval.

Dataset containing reaction times to the simultaneous presentation of personally familiar faces, stranger faces, or objects.

There is increasing interest in clarifying how different face emotion expressions are perceived by people from different cultures, of different ages and sex. However, scant availability of well-controlled emotional face stimuli from non-Western populations limit the evaluation of cultural differences in face emotion perception and how this might be modulated by age and sex differences. We present a database of East Asian face expression stimuli, enacted by young and older, male and female, Taiwanese using the Facial Action Coding System (FACS). Combined with a prior database, this present database consists of 90 identities with happy, sad, angry, fearful, disgusted, surprised and neutral expressions amounting to 628 photographs. Twenty young and 24 older East Asian raters scored the photographs for intensities of multiple-dimensions of emotions and induced affect. Multivariate analyses characterized the dimensionality of perceived emotions and quantified effects of age and sex. We also applied commercial software to extract computer-based metrics of emotions in photographs. Taiwanese raters perceived happy faces as one category, sad, angry, and disgusted expressions as one category, and fearful and surprised expressions as one category. Younger females were more sensitive to face emotions than younger males. Whereas, older males showed reduced face emotion sensitivity, older female sensitivity was similar or accentuated relative to young females. Commercial software dissociated six emotions according to the FACS demonstrating that defining visual features were present. Our findings show that East Asians perceive a different dimensionality of emotions than Western-based definitions in face recognition software, regardless of age and sex. Critically, stimuli with detailed cultural norms are indispensable in interpreting neural and behavioral responses involving human facial expression processing. To this end, we add to the tools, which are available upon request, for conducting such research.

Familiarity alters face recognition: Familiar faces are recognized more accurately than unfamiliar ones and under difficult viewing conditions when unfamiliar face recognition fails. The neural basis for this fundamental difference remains unknown. Using whole-brain functional magnetic resonance imaging, we found that personally familiar faces engage the macaque face-processing network more than unfamiliar faces. Familiar faces also recruited two hitherto unknown face areas at anatomically conserved locations within the perirhinal cortex and the temporal pole. These two areas, but not the core face-processing network, responded to familiar faces emerging from a blur with a characteristic nonlinear surge, akin to the abruptness of familiar face recognition. In contrast, responses to unfamiliar faces and objects remained linear. Thus, two temporal lobe areas extend the core face-processing network into a familiar face-recognition system.

The data are the result of an experiment with composite faces. This is a standard composite face task in which a study and test composite faces are presented one after the other. The participant's task is to indicate whether the top half of the test and study faces are 'same' or 'different'. Both RTs and accuracy are measured.

ReactionGIF

From https://github.com/bshmueli/ReactionGIF

  Excerpt from original repo readme

ReactionGIF is a unique, first-of-its-kind dataset of 30K sarcastic tweets and their GIF reactions. To find out more about ReactionGIF, check out our ACL 2021 paper:

Shmueli, Ray and Ku, Happy Dance, Slow Clap: Using Reaction GIFs to Predict Induced Affect on Twitter

  Citation

If you use our dataset, kindly cite the paper using the following BibTex entry:… See the full description on the dataset page: https://huggingface.co/datasets/julien-c/reactiongif.

We present BIRAFFE, a dataset consisting of electrocardiogram (ECG), galvanic skin reaction (GSR) and changes in facial expression signals recorded during affect elicitation by means of audio-visual stimuli (from IADS and IAPS databases) and our two proof-of-concept affective games ("Affective SpaceShooter 2" and "Fred Me Out 2"). All the signals were captured using portable and low-cost equipment: BITalino (r)evolution kit for ECG and GSR, and Creative Live! web camera for face photos (further analyzed by MS Face API).

Besides the signals, the dataset consists also of subjects' self-assessment of their affective state after each stimuli (with the use of two widgets: the first one with 5 emoticons and the second with valence and arousal dimensions) and "Big Five" personality traits assessment (using NEO-FFI inventory).

For detailed description see BIRAFFE-AfCAI2019-paper.pdf. For preview of the files before downloading the whole dataset see sample-SUB1107-[...] files.

All documents and papers that report on research that uses the BIRAFFE dataset should acknowledge this by citing the paper: Kutt, K., Drążyk, D., Jemioło, P., Bobek, S., Giżycka, B., Rodriguez-Fernandez, V., & Nalepa, G. J. (2020). "BIRAFFE: Bio-Reactions and Faces for Emotion-based Personalization". In G. J. Nalepa, J. M. Ferrandez, J. Palma, & V. Julian (Eds.), Proceedings of the 3rd Workshop on Affective Computing and Context Awareness in Ambient Intelligence (AfCAI 2019) (CEUR-WS Vol. 2609). http://ceur-ws.org/Vol-2609/

Our research related to the BIRAFFE dataset is summarized in: Kutt, K., Drążyk, D., Bobek, S., & Nalepa, G. J. (2021). "Personality-Based Affective Adaptation Methods for Intelligent Systems". Sensors, 21(1), 163. https://doi.org/10.3390/s21010163

ammarnasr/Python-React-Code-Dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Computational model fits to human neuroimaging data. Please see included readme.txt file.

This data is for the article “ Face Mask Reduces Gaze-Cueing Effect”. This dataset contains the raw data and process data of reaction time and accuracy of Experiment 1 and 2.

Dataset: AFFEC - Advancing Face-to-Face Emotion Communication Dataset

Overview

The AFFEC (Advancing Face-to-Face Emotion Communication) dataset is a multimodal dataset designed for emotion recognition research. It captures dynamic human interactions through electroencephalography (EEG), eye-tracking, galvanic skin response (GSR), facial movements, and self-annotations, enabling the study of felt and perceived emotions in real-world face-to-face interactions. The dataset comprises 84 simulated emotional dialogues, 72 participants, and over 5,000 trials, annotated with more than 20,000 emotion labels.

Dataset Structure

The dataset follows the Brain Imaging Data Structure (BIDS) format and consists of the following components:

Root Folder:

• sub-* : Individual subject folders (e.g., sub-aerj, sub-mdl, sub-xx2)
• dataset_description.json: General dataset metadata
• participants.json and participants.tsv: Participant demographics and attributes
• task-fer_events.json: Event annotations for the FER task
• README.md: This documentation file

Subject Folders (sub-):

Each subject folder contains:

• Behavioral Data (beh/): Physiological recordings (eye tracking, GSR, facial analysis, cursor tracking) in JSON and TSV formats.
• EEG Data (eeg/): EEG recordings in .edf and corresponding metadata in .json.
• Event Files (*.tsv): Trial event data for the emotion recognition task.
• Channel Descriptions (*_channels.tsv): EEG channel information.

Data Modalities and Channels

1. Eye Tracking Data

• Channels: 16 (fixation points, left/right eye gaze coordinates, gaze validity)
• Sampling Rate: 62 Hz
• Trials: 5632
• File Example: sub-

2. Pupil Data

• Channels: 21 (pupil diameter, eye position, pupil validity flags)
• Sampling Rate: 149 Hz
• Trials: 5632
• File Example: sub-

3. Cursor Tracking Data

• Channels: 4 (cursor X, cursor Y, cursor state)
• Sampling Rate: 62 Hz
• Trials: 5632
• File Example: sub-

4. Face Analysis Data

• Channels: Over 200 (2D/3D facial landmarks, gaze detection, facial action units)
• Sampling Rate: 40 Hz
• Trials: 5680
• File Example: sub-

5. Electrodermal Activity (EDA) and Physiological Sensors

• Channels: 40 (GSR, body temperature, accelerometer data)
• Sampling Rate: 50 Hz
• Trials: 5438
• File Example: sub-

6. EEG Data

• Channels: 63 (EEG electrodes following the 10-20 placement scheme)
• Sampling Rate: 256 Hz
• Reference: Left earlobe
• Trials: 5632
• File Example: sub-

7. Self-Annotations

• Trials: 5807
• Annotations Per Trial: 4
• Event Markers: Onset time, duration, trial type, emotion labels
• File Example: task-fer_events.json

Experimental Setup

Participants engaged in a Facial Emotion Recognition (FER) task, where they watched emotionally expressive video stimuli while their physiological and behavioral responses were recorded. Participants provided self-reported ratings for both perceived and felt emotions, differentiating between the emotions they believed the video conveyed and their internal affective experience.

The dataset enables the study of individual differences in emotional perception and expression by incorporating Big Five personality trait assessments and demographic variables.

Usage Notes

• The dataset is formatted in ASCII/UTF-8 encoding.
• Each modality is stored in JSON, TSV, or EDF format as per BIDS standards.
• Researchers should cite this dataset appropriately in publications.

Applications

AFFEC is well-suited for research in:

• Affective Computing
• Human-Agent Interaction
• Emotion Recognition and Classification
• Multimodal Signal Processing
• Neuroscience and Cognitive Modeling
• Healthcare and Mental Health Monitoring

Acknowledgments

This dataset was collected with the support of brAIn lab, IT University of Copenhagen.
Special thanks to all participants and research staff involved in data collection.

License

This dataset is shared under the Creative Commons CC0 License.

Contact

For questions or collaboration inquiries, please contact [brainlab-staff@o365team.itu.dk].

Here are a few use cases for this project:

1. Security and Surveillance: The "FLASH MDP Face Detection" can be employed in security systems to identify individuals in public spaces or private buildings. This can help prevent unauthorized access and keep track of people's movement in monitored areas.

2. Driver Monitoring Systems: The model can be used in advanced driver assistance systems (ADAS) and autonomous vehicles to recognize and monitor drivers' faces to ensure attentiveness, detect drowsiness or distraction, and provide timely alerts to enhance road safety.

3. Social Media and Photo Management: The face detection model can be utilized in social media platforms and photo management applications to automatically recognize and tag faces, enabling users to effortlessly search for people, organize their images, and create personalized albums.

4. Retail and Marketing Analytics: The "FLASH MDP Face Detection" can be integrated into retail environments and digital signage systems to analyze customers' faces, helping businesses gather insights on demographics, emotional responses to products, and preferred store layouts, ultimately improving the customer experience.

5. Smart Access and Attendance Systems: The model can be implemented in workplaces, schools, and conferences, where facial recognition is used as a means of access control or attendance tracking. This would save time, streamline operations, and enhance security while maintaining user privacy.

Visible and thermal images have been acquired using a thermographic camera TESTO 880-3, equipped with an uncooled detector with a spectral sensitivity range from 8 to 14 μm and provided with a germanium optical lens, and an approximate cost of 8.000 EUR. For the NIR a customized Logitech Quickcam messenger E2500 has been used, provided with a Silicon based CMOS image sensor with a sensibility to the overall visible spectrum and the half part of the NIR (until 1.000 nm approximately) with a cost of approx. 30 EUR. We have replaced the default optical filter of this camera by a couple of Kodak daylight filters for IR interspersed between optical and sensor. They both have similar spectrum responses and are coded as wratten filter 87 and 87C, respectively. In addition, we have used a special purpose printed circuit board (PCB) with a set of 16 infrared leds (IRED) with a range of emission from 820 to 1.000 nm in order to provide the required illumination.

The thermographic camera provides a resolution of 160×120 pixels for thermal images and 640×480 for visible images, while the webcam provides a still picture maximum resolution of 640×480 for near-infrared images and this has been the final resolution selected for our experiments.

A couple of halogen focus disposed 30 degrees away from the frontal direction and about 3 m away from the user, match the artificial light of the room. Note that all the tripods and structures have fixed markings on the ground.

react-vl/react-retrieval-datasets dataset hosted on Hugging Face and contributed by the HF Datasets community