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Rishabh Shukla

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Rishabh Shukla

Researcher
Department of Computer Science

shukla.rishab@ntnu.no
ResearchGate Google Scholar Website
About Research Publications

About

I am a researcher at NTNU in the Department of Computer Science. My research is on privacy-preserving biometrics and computer vision. I am mainly interested in how generative models can be used to protect biometric data while keeping it useful for recognition.

Contact: shukla.rishab@ntnu.no
Google Scholar: https://scholar.google.com/citations?user=woCrno8AAAAJ

Research

Biometric data such as fingerprints, faces, and irises cannot be changed if they are leaked. At the same time, privacy regulations make it difficult to collect and share the large datasets that modern recognition systems need. My research looks at how generative models can help with both problems.

Research interests: biometrics, privacy-preserving AI, cancelable biometrics, synthetic data, generative models (GANs and diffusion models), image inpainting, medical image anonymization, and multimodal recognition.

Main research topics

  • Cancelable and proxy biometric templates. Generating proxy fingerprints linked with a user-specific key. If a template is stolen, it can be cancelled and replaced, and matching still works with standard fingerprint matchers.
  • Synthetic biometric data. Generating realistic fingerprint datasets for training and testing without collecting data from real people, including diffusion-based generation of contactless fingerprints.
  • Inpainting and anonymization. Face and fingerprint inpainting, video inpainting, and anonymization of retinal images that removes identity information while keeping the features needed to diagnose diabetic retinopathy.
  • Evaluation of multimodal recognition. Fair evaluation protocols for open-set recognition using biometrics.

Current work: a common framework for evaluating privacy and recognition accuracy of protected and synthetic biometrics, and extending proxy template generation from fingerprints to face and iris.

Publications

R. Shukla, H. Kaur, K. Raja. TRIOCULAR-ID: Evidence-Matched and Ranking-Controlled Evaluation of Open-Set Face and Bilateral-Iris Fusion. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2027.

T. Chettaoui, G. Özgür, E. Caldeira, …, R. Shukla, … IJCB-AFMFR 2026: Competition on Adapting Foundation Models for Face Recognition Using Synthetic Training Data. IEEE International Joint Conference on Biometrics (IJCB), 2026.

R. Shukla et al. RidgeFusion: Frequency-Split Conditioning for Diffusion-Based Contactless Fingerprint Rendering. International Joint Conference on Neural Networks (IJCNN), 2026.

R. Shukla, U. Pansuriya, H. Kaur. 2VA: Vessel-Aware Retinal Image Anonymization that Preserves Diabetic Retinopathy Utility. VISAPP, 2026.

R. Shukla, H. Kaur. Restoring Motion: A Video Inpainting Method for Moving Instances. ICDCIT, 2026.

R. Shukla, H. Kaur, I. Echizen. Analyzing Face Image Inpainting with Attribute-Driven Generative Network. IJCNN, 2025.

R. Shukla, H. Kaur, I. Echizen. Parallel Prints: Generating Realistic Cancelable Fingerprint Templates. WACV Workshops, 2025.

A. K. Yadav, R. Shukla, H. Kaur. Ship Classification Using Optical Camera and Deep Learning for Coastal Surveillance. ICMVA, 2025.

R. Shukla, H. Kaur, I. Echizen. Bridging the Gap: Creating Authentic Biometric Templates for Secure Authentication Systems. IEEE SMC, 2024.

R. Shukla, A. Sinha, V. Singh, H. Kaur. Vikriti-ID: A Novel Approach for Real Looking Fingerprint Data-set Generation. WACV, 2024.

H. Kaur, R. Shukla, I. Echizen, P. Khanna. Secure and Privacy Preserving Proxy Biometric Identities. AINA, 2023.

R. Shukla, H. Kaur, I. Echizen, P. Khanna. Fingerprint Digital Twin for Secure and Privacy Preserving Biometric Authentication. IEEE SMC, 2023.

R. Shukla, H. Kaur. Deep Learning Based Cancelable Biometric System. ICCCNT, 2023.

R. Shukla, H. Kaur. An Experimental Study on Random Projection Based Biometric Security. IEEE BigData, 2023.

R. Shukla, H. Kaur. Optical Coherence Tomography Classification Through Deep Learning. IEEE GlobConPT, 2022.

R. Shukla, H. Kaur. Rural Electrification Using Computer Vision and Wireless Sensor Networks. CONIT, 2022.

Full list: https://scholar.google.com/citations?user=woCrno8AAAAJ

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