AIOmarRehan/Cropped_Yale_Faces

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Cropped Yale Face Dataset (Grayscale)

A clean and standardized version of the Cropped Yale Facial Image Dataset, containing grayscale 168×192 cropped facial images captured under controlled illumination conditions. This dataset is widely used for:

• Face recognition
• Illumination-invariant modeling
• Classical computer vision research
• Autoencoders & generative models

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Overview

The Cropped Yale Face Dataset is derived from the original Yale Face Database B.

This version contains:

• 28 human subjects
• Frontal face images only
• Strong illumination variations from many light source directions
• Aligned, cropped, grayscale images

Ideal for:

• Face recognition experiments
• Light normalization research
• PCA/LDA classical ML tasks
• Autoencoders, GANs, and image reconstruction tasks

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Dataset Structure

dataset/
│
├── yaleB01/
│   ├── yaleB01_P00A+000E+00.pgm
│   ├── yaleB01_P00A+000E+01.pgm
│   └── ...
│
├── yaleB02/
│   ├── yaleB02_P00A+000E+00.pgm
│   ├── yaleB02_P00A+000E+01.pgm
│   └── ...
│
└── ...

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File Format

Property	Value
Image size	168 × 192
Color mode	Grayscale
File type	.png / .jpg
Subjects	28
Images per subject	~64

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Example Usage

Load Images with Python (Hugging Face Datasets)

from datasets import load_dataset
import matplotlib.pyplot as plt

ds = load_dataset("YOUR_USERNAME/cropped-yale")

sample = ds["train"][0]["image"]
plt.imshow(sample, cmap="gray")
plt.axis("off")

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TensorFlow Preprocessing Example

import tensorflow as tf

def preprocess(img):
    img = tf.image.resize(img, (192, 168))
    img = tf.cast(img, tf.float32) / 255.0
    return img

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PyTorch Example

from torchvision import transforms

transform = transforms.Compose([
    transforms.Resize((192, 168)),
    transforms.ToTensor()
])

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Applications

Face Recognition

Train classical or modern models:

• Eigenfaces
• Fisherfaces
• SVM classifiers
• CNN-based architectures

Illumination-Invariant Face Analysis

Evaluate model robustness under extreme lighting shifts.

Dimensionality Reduction

Perfect for:

• PCA
• LDA
• Linear subspace modeling

Autoencoders / GANs

Great for:

• Reconstruction
• Denoising
• Generative modeling

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Sample Images

(Optional — you may add examples here)

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Download / Use

If you're viewing this on Hugging Face, simply click:

Use dataset → Load in Python

Or install via:

load_dataset("YOUR_USERNAME/cropped-yale")

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License & Citation

This dataset is derived from:

Yale Face Database B and the Cropped Yale Dataset, prepared by Yale University researchers.

If you use this dataset in academic work, please cite:

Georghiades, A. S., Belhumeur, P. N., & Kriegman, D. J. From Few Pixels to the Illumination Cone Model. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2001.

All images are provided for research and academic purposes only.

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Acknowledgements

Special thanks to Yale University for releasing the original dataset and supporting reproducible computer vision research.

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