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Generating Diverse and Controllable Synthetic Fingerprints Using Multimodal Diffusion Models

Core Concepts
GenPrint, a controllable latent diffusion model, can generate diverse and realistic synthetic fingerprint images with explainable control over fingerprint class, acquisition type, sensor device, and quality level, including zero-shot generation of fingerprint styles not seen during training.
The paper presents GenPrint, a framework for generating diverse and controllable synthetic fingerprint images using a multimodal latent diffusion model. Key highlights: GenPrint can generate fingerprint images of various types (rolled, slap, swipe, contactless, latent) while maintaining identity and offering humanly understandable control over appearance factors like fingerprint class, acquisition type, sensor device, and quality level. Unlike previous approaches, GenPrint is not confined to replicating style characteristics from the training dataset alone. It enables the generation of novel fingerprint styles from unseen devices without requiring additional fine-tuning. GenPrint uses latent diffusion models with multimodal conditions (text and image) to achieve consistent generation of style and identity. The text prompts provide explainable control over appearance factors, while the image style embeddings capture intra-class variations not easily expressed in language. Experiments demonstrate the benefits of GenPrint in terms of identity preservation, explainable control, and universality of generated images. GenPrint-generated images yield comparable or superior accuracy to models trained solely on real data and further enhance performance when augmenting the diversity of existing real fingerprint datasets. GenPrint images can also be used to replace real fingerprint data for large-scale identification experiments, showcasing their utility for evaluating fingerprint recognition systems.
GenPrint-generated fingerprints have an average minutiae count of 42.45 ± 8.52, compared to 37.18 ± 9.75 for real fingerprints. The average minutiae quality of GenPrint-generated fingerprints is 80.40 ± 9.83, compared to 80.38 ± 10.36 for real fingerprints. The average fingerprint area of GenPrint-generated images is 211,599 ± 19,241 pixels, compared to 192,285 ± 34,368 pixels for real fingerprints.
"GenPrint is capable of generating fingerprints of any acquisition type, sensor, fingerprint class, and quality, including fingerprint styles not seen during training without any additional fine-tuning (e.g., zero-shot fingerprint style generation)." "The generation process is controllable (both in appearance and identity preservation) and explainable with humanly interpretable text prompts."

Deeper Inquiries

How can GenPrint's generation capabilities be extended to other biometric modalities beyond fingerprints, such as face, iris, or palm print

GenPrint's generation capabilities can be extended to other biometric modalities beyond fingerprints by adapting the framework to the specific characteristics and features of each modality. For example: Face Recognition: For face biometrics, GenPrint can be modified to generate facial images with controllable factors such as age, gender, facial expression, and lighting conditions. By incorporating text prompts related to these factors, the model can generate diverse facial images for training face recognition systems. Iris Recognition: To generate synthetic iris images, GenPrint can be adjusted to include parameters such as iris texture, pupil dilation, and iris color. By providing text prompts specifying these attributes, the model can produce realistic and varied iris images for iris recognition algorithms. Palm Print Recognition: Similar to fingerprints, palm prints have unique ridge patterns that can be controlled and varied in synthetic images. GenPrint can be tailored to generate palm print images with factors like palm size, crease patterns, and quality levels, enabling the training of palm print recognition models. By customizing the input conditions and training data for each biometric modality, GenPrint can be adapted to generate synthetic data for various biometric recognition systems, enhancing their performance and diversity.

What are the potential privacy and ethical concerns around the use of synthetic biometric data, and how can they be addressed

Privacy and ethical concerns surrounding the use of synthetic biometric data include: Data Security: Synthetic biometric data, if not properly secured, could be vulnerable to unauthorized access and misuse, leading to identity theft or fraud. Biometric Template Protection: There is a risk of reverse engineering synthetic biometric templates to reconstruct original biometric data, compromising individuals' privacy. Ethical Use: Synthetic biometric data should be used ethically and responsibly to prevent potential misuse, discrimination, or harm to individuals. Consent and Transparency: Individuals should be informed about the generation and use of synthetic biometric data, and their consent should be obtained before its collection or processing. To address these concerns: Encryption and Access Control: Implement robust encryption and access control measures to protect synthetic biometric data from unauthorized access. Anonymization: Ensure that synthetic biometric data is anonymized to prevent the identification of individuals from the generated data. Compliance with Regulations: Adhere to data protection regulations such as GDPR and biometric privacy laws to safeguard synthetic biometric data. Ethical Guidelines: Develop and follow ethical guidelines for the generation, storage, and use of synthetic biometric data to ensure responsible and transparent practices. By implementing these measures, organizations can mitigate privacy and ethical risks associated with synthetic biometric data usage.

Could the diffusion-based approach used in GenPrint be applied to generate other types of synthetic data, such as medical images or satellite imagery, while maintaining controllability and realism

The diffusion-based approach used in GenPrint can be applied to generate other types of synthetic data, such as medical images or satellite imagery, while maintaining controllability and realism by: Dataset Selection: Curating diverse and representative datasets of medical images or satellite imagery to train the diffusion model on a wide range of variations and styles. Text and Image Conditions: Incorporating text prompts and image embeddings specific to the characteristics of medical images or satellite imagery to control factors like pathology type, imaging modality, or geographic features. Quality Control: Implementing quality control mechanisms to ensure the generated synthetic data meets the required standards in terms of resolution, clarity, and accuracy. Privacy Preservation: Applying techniques like data anonymization and encryption to protect sensitive information in medical images and satellite imagery. Validation and Evaluation: Conducting thorough validation and evaluation of the generated synthetic data against real-world datasets to ensure realism and utility for downstream applications. By adapting the diffusion model and training process to the unique attributes of medical images or satellite imagery, the approach used in GenPrint can be extended to generate high-quality synthetic data in these domains for various applications in healthcare, remote sensing, and research.