Augmented Olivetti Faces Dataset

Dataset Composition:

This dataset contains 2000 facial images, derived from the original 400 grayscale images that captured various facial expressions under standardized lighting. Each individual in the dataset has 50 unique images, created through multiple augmentation techniques. These techniques include:

• Horizontal flipping
• Rotation by small angles
• Cropping
• Resizing
• Addition of controlled noise

These augmentations aim to simulate real-world distortions, ensuring the diversity and variability necessary for robust model training.

Technical Details:

Each image is represented as a 2D matrix of pixel values, allowing seamless integration into computer vision tasks. The augmentations preserve key facial features while introducing variability, providing valuable training data for algorithms. The carefully crafted augmentations replicate common distortions and transformations that are frequently encountered in real-world applications.

Applications:

The dataset is especially valuable for researchers and developers working on:

• Facial recognition systems
• Image inpainting techniques
• Facial expression analysis
• General computer vision tasks

Researchers can use both the original and augmented images to test the impact of various augmentations on model performance, generalization capabilities, and robustness.

Benefits for Researchers and Developers:

The Augmented Olivetti Faces Dataset is particularly useful for evaluating algorithms in diverse conditions, improving adaptability to unseen scenarios. With structured augmentations, it provides an ideal testing ground for exploring:

• Data augmentation techniques
• Model performance evaluation
• Facial recognition and image processing advancements