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Facial Emotion Recognition Datasets for YOLOv8 Annotation

Alameer, Ali

Authors

Dr Ali Alameer A.Alameer1@salford.ac.uk
Lecturer in Artificial Intelligence



Abstract

Computer Vision Scientist-Collected Dataset:

This facial emotion recognition dataset has been meticulously curated by computer vision scientists using mobile phone cameras to capture candid moments of individuals expressing a spectrum of emotions, including Happy, Sad, Fear, and Humor. The dataset comprises a rich collection of images with diverse angles and backgrounds, providing a realistic portrayal of human emotional expression.

Marketing Expert-Collected Dataset:

The ethnicity-focused dataset for facial recognition has been meticulously assembled by marketing experts, aiming to shed light on the vital aspect of ethnicity variations in computer vision. With a dedicated focus on ethnicity, this dataset provides a unique perspective for training and testing facial recognition models in an ethnically diverse context.

This dataset comprises a rich collection of images capturing individuals from various ethnic backgrounds. It emphasises the importance of ethnicity in the field of computer vision and includes a wide range of facial features, expressions, and poses, thereby enriching the dataset's diversity.

By offering insights into the critical area of ethnicity in computer vision, this dataset is a valuable addition to the toolkit of researchers and practitioners, facilitating the development of more inclusive and accurate facial recognition models.

Researchers and experts in the fields of computer vision and marketing are encouraged to explore these datasets for their research, model development, and the advancement of understanding in these respective domains.

Online Publication Date Oct 12, 2023
Publication Date Oct 12, 2023
Deposit Date Jan 20, 2025
DOI https://doi.org/10.17866/rd.salford.24192219.v1
Publisher URL https://salford.figshare.com/articles/dataset/_b_Facial_Emotion_Recognition_Datasets_for_YOLOv8_Annotation_b_/24192219
Collection Date Oct 12, 2023