---
title: Human Face Filter Tool
license: other
tags:
- image-processing
- face-detection
- dataset-cleaning
- opencv
- yunet
- python
pretty_name: Human Face Filter Tool
---

# Human Face Filter Tool

A simple local dataset cleaning tool for detecting and separating images that contain human faces.

This tool scans a local `dataset` folder using OpenCV YuNet face detection and moves images with detected human faces into a separate `removed_human_faces` folder.

It is designed for dataset preparation workflows where human face images should be filtered out before uploading or using the cleaned dataset.

## Features

* Detects human faces using OpenCV YuNet
* Moves detected face images outside the dataset folder
* Keeps the original folder structure when moving files
* Supports common image formats such as JPG, PNG, WEBP, BMP, and TIFF
* Uses strict landmark geometry checks to reduce false positives
* Safer default behavior: files are moved, not deleted

## Folder Structure

```text
human-face-filter-tool/
├─ dataset/
├─ face_detection_yunet.onnx
├─ remove_face_images.py
├─ remove_face_images.bat
├─ requirements.txt
└─ README.md
```

After running the script, images with detected human faces will be moved to:

```text
removed_human_faces/
```

## Installation

Install the required Python packages:

```bash
pip install -r requirements.txt
```

## Usage

Place the images you want to scan inside the `dataset` folder.

Then run:

```bash
python remove_face_images.py
```

On Windows, you can also run:

```bash
remove_face_images.bat
```

## Output

The script scans all supported image files inside:

```text
dataset/
```

Images that contain detected human faces are moved to:

```text
removed_human_faces/
```

Images without detected human faces remain inside:

```text
dataset/
```

## Important Notes

This tool does not modify the image content. It only moves images that are detected as containing human faces.

By default, files are not deleted. They are moved to `removed_human_faces` for manual review.

Do not upload the `removed_human_faces` folder if your goal is to publish only the cleaned dataset.

## Detection Settings

The script uses strict filtering settings to reduce false positives, especially for nature, animal, texture, and object images.

Main safety settings include:

```python
SCORE_THRESHOLD = 0.80
MIN_FACE_AREA_RATIO = 0.0015
MAX_IMAGE_SIDE_FOR_SCAN = 1280
DELETE_INSTEAD_OF_MOVE = False
```

You can adjust these values inside `remove_face_images.py` if needed.

## Recommended Workflow

1. Put your raw images inside the `dataset` folder.
2. Run the script.
3. Review the `removed_human_faces` folder.
4. Keep only the cleaned `dataset` folder for your final dataset workflow.

## Requirements

* Python 3.9+
* OpenCV
* NumPy
* Pillow
* tqdm

Install all requirements with:

```bash
pip install -r requirements.txt
```

## License

Please check the license terms of the YuNet ONNX model and any datasets you process with this tool.

## Disclaimer

Face detection may not be perfect. Always manually review important datasets before publishing, training, or distribution.