Dataset of 213 Grayscale Images Featuring 7 Positive Facial Expressions from 10 Individuals

The database consists of 213 grayscale images capturing 7 positive facial expressions from 10 individuals. All images are stored as 256×256 pixel 8-bit grayscale TIFF format files. On average, each subject has 2 to 4 image samples per expression category.

This dataset was specifically created for facial expression recognition research. By collecting expression images from diverse individuals, researchers can analyze and compare facial characteristics across different emotional states. The dataset supports algorithm development for emotion understanding and human-social interaction studies, with potential applications in convolutional neural networks (CNNs) for feature extraction and classification.

Each facial expression image undergoes rigorous selection and annotation protocols to ensure data quality and accuracy. These standardized images are suitable for training and testing expression recognition algorithms, particularly through machine learning pipelines involving image preprocessing, feature dimensionality reduction, and supervised classification models to enhance facial recognition and affective computing performance.

The database establishment aims to advance research in facial expression recognition. It provides researchers with a valuable resource for developing robust algorithms, potentially utilizing techniques like Local Binary Patterns (LBP) for texture analysis or deep learning architectures for automatic feature learning.

In summary, this dataset contains 213 grayscale images specifically designed for facial expression recognition research. It enables in-depth study of human expression characteristics and supports the development of advanced computer vision algorithms through standardized data benchmarking and cross-validation methodologies.