Research Vision

Advancing reliable AI systems for recognition, vision, and anomaly detection.

I aim to design machine learning and computer vision methods that are reliable, robust, and generalisable. My research sits at the intersection of theory and practice, where I develop principled approaches grounded in statistical learning, kernel methods and deep learning approaches, and translate them into practical solutions for biometrics, anomaly detection, and time-series analysis.

The unifying theme of my work is to build AI systems that can:

• operate with limited supervision,

• remain effective under unseen conditions, and

• adapt to new and evolving challenges such as deepfakes and adversarial attacks.

Core research interests: one-class classification, kernel-based learning, Deep Learning and sparse modelling.

Research Themes

• Face Presentation Attack Detection

• Deepfake and Manipulation Detection

• One-Class and Open-Set Recognition

• Ensemble and Sparse Fusion

• Sequential/Time-Series Anomaly Detection

Key Contributions

• lp-norm constrained one-class classifier fusion (Information Fusion, 2025)

• Large-margin multiple-kernel SVDD (Pattern Recognition, 2024)

• One-class multiple kernel Fisher null approach (IEEE Transactions on Image Processing, 2023)

• Robust one-class kernel spectral regression (IEEE Transactions on Neural Networks and Learning Systems, 2021)

Key Projects

• Deepfake Detection Using One-Class and Open-Set Recognition

Current Directions

• Semi-supervised deepfake detection

• Sparsity-induced adaptive classifier fusion

• Anomaly detection in sequential data streams

Collaborations

• Long-term collaboration with Prof. Josef Kittler (CVSSP, University of Surrey)