This is the cropped version of "The Extended Yale Face Database B" The extended Yale Face Database B contains 16128 images of 28 human subjects under 9 poses and 64 illumination conditions. The data format of this database is the same as the Yale Face Database B. Please refer to the homepage of the Yale Face Database B for more detailed information of the data format. You are free to use the extended Yale Face Database B for research purposes. All publications which use this database should acknowledge the use of "the Exteded Yale Face Database B" and reference Athinodoros Georghiades, Peter Belhumeur, and David Kriegman s paper, "From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose", PAMI, 2001. The extended database as opposed to the original Yale Face Database B with 10 subjects was first reported by Kuang-Chih Lee, Jeffrey Ho, and David Kriegman in "

The Extended Yale B database consists of 2414 frontal-face images of 38 subjects. Each subject has around 64 images. The images are cropped and normalized to 192 × 168 under various laboratory-controlled lighting conditions.

The Yale face database is a face dataset, mainly used for identification, which contains 15 subjects, each of which has 11 images, a total of 165 grayscale images in GIF format, and each subject contains different facial expressions: Center light, with glasses, happy, left light, without glasses, normal, right light, sad, sleepy, surprised and wink. This dataset was released by Yale University in 2001.

Accuracy on a subset of the Extended Yale Face Database B.

Same as cropped images here, just converted to PNG instead http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html

I do not own this data. All credits go to:

"From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose", PAMI, 2001, "Acquiring Linear Subspaces for Face Recognition under Variable Lighting", PAMI, May, 2005 "the Extended Yale Face Database B"

The cropped dataset only contains the single P00 pose.

Data format is like yaleBxx_P00A(+/-)aaaE(+/-)ee

• xx = Subject ID
• (+/-)aaa = Azimuth angle
• (+/-)ee = Elevation angle

For example the file yaleB38_P00A+035E+65.png is of subject 38, in pose 00, with light source at (+035, +65) degrees (azimuth, elevation) w.r.t the camera.

Context

This database of faces was downloaded from YALE University in the United States.

Content

The Yale Face Database (size 6.4MB) contains 165 grayscale images in GIF format of 15 individuals. There are 11 images per subject, one per different facial expression or configuration: center-light, w/glasses, happy, left-light, w/no glasses, normal, right-light, sad, sleepy, surprised, and wink.

Acknowledgements

http://vision.ucsd.edu/content/yale-face-database

A comparison of face recognition rates on Extended Yale B database.

Dataset

This dataset was created by Jiyao Liu

Contents

Performance comparison on the Yale face database (results of our proposed algorithm are in bold).

Recognition rates (%) on the Extended Yale B database with block occlusion.

This directory contains benchmark face databases (AR, FERET, GT, ORL, and Yale) used to evaluate our proposed RSSPCA algorithm in comparison with established methods including PCA, PCA-L1, and RSPCA.

timathom/yale-library-entity-resolver-training-data dataset hosted on Hugging Face and contributed by the HF Datasets community

Algorithms compared in our experiments on the Yale database.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The recognition rate of each classifier for face recognition on the Extended Yale B database.

The average recognition rates (%) and standard deviations (%) of different algorithms on Yale database.

Schizophrenia is a severe psychiatric disorder associated with a wide range of cognitive and neurophysiological dysfunctions and long-term social difficulties. Early detection is expected to reduce the burden of disease by initiating early treatment. In this paper, we test the hypothesis that the integration of multiple simultaneous acquisitions of neuroimaging, behavioral, and clinical information will be better for the prediction of early psychosis than unimodal recordings. We propose a novel framework to investigate the neural underpinnings of the early psychosis symptoms (that can develop into Schizophrenia with age) using multimodal acquisitions of neural and behavioral recordings including functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), and facial features. Our data acquisition paradigm is based on live face-to-face interaction in order to study the neural correlates of social cognition in first-episode psychosis (FEP). We propose a novel deep repre..., The proposed method employs dyads that include one individual who serves as the live expressive face stimulus and the other partner categorized as either typically developed (TD) or first episode psychosis (FEP) patient. Dyads faced each from across a table at a distance of approximately 140 cm and table-mounted eye-tracking systems were positioned to measure continuous eye movements of the subject. Functional NIRS and EEG data were also synchronized and continuously acquired hemodynamic and electrocortical responses of the subject during the experiment. The dyads were separated by a “smart glass†in the center of the table that controlled face gaze times (the glass was transparent during gaze periods) and “rest times†(the glass was opaque during rest) (Hirsch, X. Zhang, Noah, and Bhattacharya, 2023). Paradigm The dyads were seated 140 cm across a table from each other. A "Smart Glass" (glass that is capable of alternating its appearance between opaque and transparent upon applica..., , # Deep multimodal representations and classification of first-episode psychosis via live face processing

This readme file was generated on 2025-02-18 by Rahul Singh

GENERAL INFORMATION

Author Information Name: Rahul Singh Institution: Yale University Email: r.singh@yale.edu

Principal Investigator Information Name: Joy Hirsch ORCID: 0000-0002-1418-6489 Institution: Yale School of Medicine Email: joy.hirsch@yale.edu

Principal Investigator Information Name: Smita Krishnaswamy Institution: Wu Tsai Institute, Yale University Email: smita.krishnaswamy@yale.edu

Author/Alternate Contact Information Name: J. Adam Noah ORCID: 0000-0001-9773-2790 Institution: Yale School of Medicine Email: adam.noah@yale.edu

Date of data collection: Approximate collection dates are 2022-01 through 2025-02.

SHARING/ACCESS INFORMATION

Recommended citation for this dataset: Hirsc...,

Dataset Card for Spider

  Dataset Summary

Spider is a large-scale complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 Yale students. The goal of the Spider challenge is to develop natural language interfaces to cross-domain databases.

  Supported Tasks and Leaderboards

The leaderboard can be seen at https://yale-lily.github.io/spider

  Languages

The text in the dataset is in English.

  Dataset Structure





  Data… See the full description on the dataset page: https://huggingface.co/datasets/xlangai/spider.

This readme file was generated on 2024-09-20 by Dr. Megan Kelley.

GENERAL INFORMATION

Title of Dataset:

Author Information Name: Megan Kelley ORCID: 0000-0001-8612-5215 Institution: Yale School of Medicine Address: 300 George Street, Suite 902, New Haven, CT, 06511, USA Email: megan.kelley@yale.edu

Principal Investigator Information Name: Joy Hirsch ORCID: 0000-0002-1418-6489 Institution: Yale School of Medicine Address: 300 George Street, Suite 902, New Haven, CT, 06511, USA Email:

Author/Alternate Contact Information Name: J. Adam Noah ORCID: 0000-0001-9773-2790 Institution: Yale School of Medicine Address: 300 George Street, Suite 902, New Haven, CT, 06511, USA Email: adam.noah@yale.edu

Date of data collection: Approximate collection dates are 2022-01-01 through 2023-02-01.

Geographic location of data collection: 300 George Str, New Haven, CT, United States.

Information about funding sources that supported the coll...

Abstract

The Ghana Socioeconomic panel household survey is a joint effort between the Economic Growth Center at Yale University and the Institute of Statistical, Social and Economic Research at Legon (Accra, Ghana). The survey is meant to remedy a major constraint on the understanding of development in low-income countries - the absence of detailed, multi-level and long-term scientific data that follows individuals over time and describes both the natural and built environment in which the individuals reside. Most data collection efforts are short-term - carried out a one point in time; are limited in scope - collecting information on only a few aspects of the lives of the persons in the study; and when there are multiple rounds of data collection, individuals who leave the study area are dropped. This latter means that the most mobile people are not included in existing surveys and studies, perhaps substantially biasing inferences about who benefits from and who bears the cost of the development process. The goal of this project, which aims to follow all individuals, or a random subset, over time using a comprehensive set of survey instruments is thus to shed new light on long-run processes of economic development.

The data from the second wave of the Ghana Socioeconomic Panel Survey covered a sample of 4,774 households containing 16,356 household members. The second wave was unique in the sense that it tracked movement of households as well as individual within a household. Thus increasing the number of households in the Panel Study due to the nature of the design; tracking wholly moved and split households. A total of 5484 households were selected for the survey comprising of 5009 households from the baseline survey and 475 households from split of households created of which 4774 households were successfully interviewed.

Geographic coverage

The survey provides regionally representative data for the 10 regions of Ghana.

Analysis unit

Households and individuals

Kind of data

Sample survey data

Mode of data collection

Face-to-face Interviews

Research instrument

The Household Questionnaire for the survey was in two parts, A and B. Questionnaire Part A collected data on household members and Questionnaire Part B collected data on the household and dwelling.