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

แนวคิดหลัก

Efficiently estimate identity, gender, and age from hand images for criminal investigations.

บทคัดย่อ

The paper proposes a multi-task representation learning framework to jointly estimate the identity, gender, and age of individuals from hand images. It explores deep learning architectures for this purpose and evaluates their performance on a dataset of 11k hands. The study aims to assist international police forces in identifying and convicting abusers based on hand image analysis.

1. Introduction to biometric identification using body parts.
2. Importance of hand images in criminal investigations.
3. Comparison of different deep learning architectures for joint estimation.
4. Description of the proposed multi-task representation learning framework.
5. Details on network architecture and loss function.
6. Performance evaluation with convolution-based and transformer-based architectures.
7. Results showing the effectiveness of the proposed method.
8. Conclusion highlighting the significance of the research.

สถิติ

We make extensive evaluations and comparisons on a publicly available 11k hands dataset.
The ViT-B-16 has almost 3 times more parameters than Swin-T i.e., 86.6M vs 28.3M.

คำพูด

"Hand images are often the only available information in cases of serious crime such as sexual abuse."
"Our proposed deep multi-task representation learning framework jointly estimates the identity, gender, and age of individuals from their hand images."

ข้อมูลเชิงลึกที่สำคัญจาก

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

by Nathanael L.... ที่ arxiv.org 03-21-2024

https://arxiv.org/pdf/2303.15263.pdf

สอบถามเพิ่มเติม

How can this multi-task approach be applied to other biometric modalities

The multi-task approach presented in the context can be applied to other biometric modalities by adapting the deep learning framework to accommodate different types of biometric data. For instance, instead of hand images, facial images could be used as input for identity, gender, and age estimation tasks. The network architecture would need to be adjusted to handle the specific characteristics and features of facial data while maintaining separate branches for each task (identity, gender, age). By training the model on a dataset containing facial images annotated with identity labels, gender information, and age groups or exact ages, it could learn to jointly estimate these attributes from facial inputs.

What ethical considerations should be taken into account when using AI for criminal investigations

When using AI for criminal investigations in the context described above, several ethical considerations must be taken into account:

Data Privacy: Ensuring that personal data collected through hand images is handled securely and anonymized properly to protect individuals' privacy.
Transparency: Providing transparency about how AI algorithms are used in investigations and ensuring accountability for their decisions.
Bias Mitigation: Addressing biases that may exist in the training data or algorithm itself to prevent discriminatory outcomes.
Consent: Obtaining informed consent from individuals whose biometric data is being used for investigation purposes.
Fairness: Ensuring fairness in how AI systems treat individuals regardless of factors like race, ethnicity, or socioeconomic status.

By considering these ethical aspects throughout the development and deployment of AI systems in criminal investigations, potential risks can be mitigated while upholding principles of justice and respect for individual rights.

How might advancements in hand image analysis impact privacy concerns related to biometric data

Advancements in hand image analysis have implications for privacy concerns related to biometric data:

Enhanced Security Measures: With improved accuracy in identifying individuals based on hand images comes a need for stronger security measures to safeguard this sensitive biometric information.
Data Protection Regulations Compliance: Organizations utilizing hand image analysis technologies must adhere to strict data protection regulations such as GDPR or HIPAA to ensure proper handling of personal information.
Informed Consent Practices: Individuals should be informed about how their hand images will be used and have control over their own biometric data through explicit consent mechanisms.
Anonymization Techniques: Implementing robust anonymization techniques when storing or sharing hand image datasets can help mitigate privacy risks associated with biometric identification methods.

Overall, advancements in hand image analysis call for a proactive approach towards addressing privacy concerns by implementing comprehensive policies and practices that prioritize individual rights and confidentiality of personal information captured through biometric modalities like hands imagery."