Core Research Specialization

Pattern Recognition

Statistical and neural approaches to recognize patterns, shapes, and structures in visual data.

2 Researchers 1 Projects 1 Publications

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In-Depth Overview

Research Focus & Scope

Pattern Recognition is fundamental to extracting invariant structures from complex, multidimensional visual data. At ICVRL, we pioneer algorithms for biometric identity verification, gait recognition, facial analysis, handwriting understanding, and structural anomaly detection.

Methodological Approach

How We Conduct Research in Pattern Recognition

Our research integrates statistical pattern recognition, metric learning (triplet loss, arcface), geometric deep learning, and manifold analysis to ensure invariant representations across varying poses, lighting conditions, and temporal sequences.

Impact & Deployments

Real-World Applications

Gait recognition from surveillance video feeds

Facial recognition and anti-spoofing biometrics

Automated handwritten script and signature verification

Structural material defect recognition

Industrial sensor pattern anomaly detection

Lab Projects

Projects in this Domain

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FaceGuard: Robust Facial Recognition Under Adverse Conditions

Advanced face recognition system resilient to occlusion, varying lighting, and pose changes for security appli...

active

Scholarly Output

Recent Publications

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journal

Robust Face Recognition Under Occlusion Using Part-Based Feature Fusion

Ayesha Malik, Ahmad Khan, Shams ur Rehman

Pattern Recognition, 2024

Tech Stack & Tools

ArcFace GaitSet Scikit-Learn PyTorch OpenCV Dlib LibSVM MATLAB

Active Researchers

Shams Rehman

Director & Senior Lecturer

Ayesha Malik

Research Assistant

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