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Research vision and interests
Advancing reliable AI systems for vision, recognition, and anomaly detection.
I am a computer vision and machine learning researcher.
My work bridges theoretical advancements and practical applications by developing robust pattern recognition and machine learning methods for:
Computer vision
Biometrics
Anomaly detection
Time-series analysis
Core research interests: One-class classification, Kernel-based learning, Sparse modelling.
My goal is to build systems that perform reliably under limited supervision and in unseen conditions.
Research areas
Face Presentation Attack Detection — Detect spoofing attempts such as masks, printed photos, and video replays.
Deepfake and Face Manipulation Detection — Classify manipulated content using one-class and open-set learning.
One-Class and Open-Set Recognition — Detect and classify when only “normal” data is available during training.
Ensemble Learning — Combine multiple sources of information at decision, feature, or score levels.
Key research projects
Ongoing and future research directions
Semi-supervised deepfake detection
Train detectors with limited labels using semi-supervised learning.
Apply one-class and open-set recognition to counter novel attack types.
Focus on domain generalisation for robustness across datasets and manipulation methods.
Sparsity-induced classifier fusion
Design sparse fusion frameworks to combine complementary classifiers.
Implement locally adaptive fusion strategies for spatially variant data.
Explore fusion-guided attention mechanisms for data-driven weighting.
Anomaly detection in sequential data streams
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