HDA-SynChildFaces-AgeTransformation

KO | EN

gitlite — search

#python #typescript #llm #go #android #deep-learning #macos #windows #awesome #cli #kubernetes #pytorch #openai #open-source

HDA-SynChildFaces-AgeTransformation

(★ 9)

No description available.

HDA-SynChildFaces-AgeTransformation 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# HDA-SynChildFaces-AgeTransformation

Official repository of the paper "*Child Face Recognition at Scale: Synthetic Data Generation and Performance Benchmark*".

## Introduction

In the paper we addressed the need for a large-scale database of children's faces by using generative adversarial networks (GANs) and face age progression (FAP) models to synthesize a realistic dataset referred to as **HDA-SynChildFaces**. The created HDA-SynChildFaces consists of 1,652 subjects and a total of 188,328 images, each subject being present at various ages and with many different intra-subject variations. An example is given below for a single subject:

The table below shows how the HDA-SynChildFaces compares to related datasets:

Contrary to other works, the HDA-SynChildFaces dataset is demographically balanced concerning several factors such as race and gender. To balance a demographic attribute, for instance, the subject's race, the subject is moved across the learned race boundaries as illustrated below.

## Download 
The database is being made available for researchers from 2024 onwards. Interested researchers can download this database. Any commercial use/distribution of this database is prohibited. All the technical reports and papers that report experimental results from this database should provide acknowledgment and reference (see the Acknowledgement Section).

Link: https://drive.google.com/drive/folders/1uoeWxrcNW9A9m9TqYStPW9tJZ1Fld2L3?usp=sharing

## Database structure
The database folder contains 5 subfolders:

* Accepted: The original adult images post filtering (20+)
* Age group 0: Transformed images with age 16-13
* Age group 1: Transformed images with age 13-10
* Age group 2: Transformed images with age 10-7
* Age group 3: Transformed images with age 7-4
* Age group 4: Transformed images with age 4-1

Furthermore, each of the above is divided into a set of reference and probe images.

Demographic attributes is given as part of the filename, following the convention:
<seed_age_sex_race>
Where seed can be seen as a type of image ID which can be used to identify the subjects across age groups. 
For a description of the database generation, refer to the paper.

## Acknowledgement

If you use this dataset or associated work, please cite the following paper:

```
@article{Falkenberg-HDASynChildFaces-Frontiers-2024,
	title        = {Child Face Recognition at Scale: Synthetic Data Generation and Performance Benchmark},
	author       = {M. Falkenberg and A. B. Ottsen and M. Ibsen and C. Rathgeb},
	year         = {2024},
	journal      = {Frontiers in Signal Processing}
}
```

HDA-SynChildFaces-AgeTransformation

HDA-SynChildFaces-AgeTransformation 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

HDA-SynChildFaces-AgeTransformation

HDA-SynChildFaces-AgeTransformation 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README