# Joint Person Identity, Gender and Age Estimation from Hand Images using Deep Multi-Task Representation Learning

arXiv: https://arxiv.org/abs/2303.15263 · HTML: https://mlsanity.com/paper/2303.15263/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 34 references that are in the map.
- First posted on arXiv 27 Mar 2023.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [MaxViT: Multi-Axis Vision Transformer](https://mlsanity.com/paper/2204.01697/) (2022)
- [A Strong Baseline and Batch Normalization Neck for Deep Person Re-identification](https://mlsanity.com/paper/1906.08332/) (2019)
- [Learning a Fixed-Length Fingerprint Representation](https://mlsanity.com/paper/1909.09901/) (2019)
- [11K Hands: Gender recognition and biometric identification using a large dataset of hand images](https://mlsanity.com/paper/1711.04322/) (2017)
- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [A Comprehensive Overview of Biometric Fusion](https://mlsanity.com/paper/1902.02919/) (2019)
- [Biometrics Recognition Using Deep Learning: A Survey](https://mlsanity.com/paper/1912.00271/) (2019)
- [Efficient Facial Representations for Age, Gender and Identity Recognition in Organizing Photo Albums using Multi-output CNN](https://mlsanity.com/paper/1807.07718/) (2018)
- [Age and Gender Prediction From Face Images Using Attentional Convolutional Network](https://mlsanity.com/paper/2010.03791/) (2020)
- [Hand-Based Person Identification using Global and Part-Aware Deep Feature Representation Learning](https://mlsanity.com/paper/2101.05260/) (2021)
- [Multi-Branch with Attention Network for Hand-Based Person Recognition](https://mlsanity.com/paper/2108.02234/) (2021)
- [Local-Aware Global Attention Network for Person Re-Identification Based on Body and Hand Images](https://mlsanity.com/paper/2209.04821/) (2022)

## Built on by (most cited first, 4 of 4)

- [Beyond Appearance: Transformer-based Person Identification from Conversational Dynamics](https://mlsanity.com/paper/2510.04753/) (2025)
- [CLIP-HandID: Vision-Language Model for Hand-Based Person Identification](https://mlsanity.com/paper/2506.12447/) (2025)
- [Two-Stream Spatial-Temporal Transformer Framework for Person Identification via Natural Conversational Keypoints](https://mlsanity.com/paper/2502.20803/) (2025)
- [Dorsal Hand Images for Immersive (XR) and Privacy-preserving Age Assurance and Child Safety](https://mlsanity.com/paper/2608.21009/) (2026)

## Abstract (arXiv)

In this paper, we propose a multi-task representation learning framework to jointly estimate the identity, gender and age of individuals from their hand images for the purpose of criminal investigations since the hand images are often the only available information in cases of serious crime such as sexual abuse. We investigate different up-to-date deep learning architectures and compare their performance for joint estimation of identity, gender and age from hand images of perpetrators of serious crime. To simplify the age prediction, we create age groups for the age estimation. We make extensive evaluations and comparisons of both convolution-based and transformer-based deep learning architectures on a publicly available 11k hands dataset. Our experimental analysis shows that it is possible to efficiently estimate not only identity but also other attributes such as gender and age of suspects jointly from hand images for criminal investigations, which is crucial in assisting international police forces in the court to identify and convict abusers.