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RESEARCH AREAS

Computational Intelligence Databases and Information Management Networking and Systems Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning research group focuses on developing the state-of-the-art models, theories and core technologies for watermarking, lip reading, speaker authentication, face recognition, biometric template protection, human action recognition and intelligent video surveillance. The research group not only aims to develop enabling technologies of all these applications, but also to address the public concerns of security and privacy issues.


Faculty Involved:


Funded Research and Consultancy Projects in the Past Few Years:

Learning Discriminative Manifolds from Multiple Data Modalities for Multimedia Content Understanding
Staff Dr. LIU, Yang
Objectives To discover the intrinsic structure of multimedia data with multiple modalities for multimedia content understanding
  • For multi-label multimedia data, learn the correlation between different labels while preserving the discriminative information.
  • For multi-modal multimedia data, learn the consistency between different modalities while preserving the discriminative information.

Interpretable Machine Learning Aided Understanding of Complex Brain Structural and Functional Interaction Underlying Spectra of Individual Differences in Cognitive Behaviour
Staff Prof. CHEUNG, Yiu Ming
Objectives
  • To develop a core set of machine and deep learning models and tools for big brain data
  • To gain new understanding of how the brain employ specific systems for selected social cognition tasks
  • To further develop and apply methods to assess autistic trait

On Developing a Lip-password Based Face Recognition System
Staff Prof. CHEUNG, Yiu Ming
Objectives
  • To integrate lip-password into the face recognition system as a single learning paradigm;
  • To select and design the underlying models and algorithms of building up the lip-password-based face recognition system.

A Hybrid Residual Network and Long Short-Term Memory Method for Peptic Ulcer Bleeding Mortality Prediction
Staff Prof. YUEN, Pong Chi
Objectives To design a hybrid residual network and long short-term memory method for accurate peptic ulcer bleeding mortality prediction.

Face anti-spoofing to combat mask attacks: A remote photoplethysmography approach
Staff Prof. YUEN, Pong Chi
Objectives
  • To effectively detect the 3D mask attack with different mask materials and qualities, a new liveness cue is proposed by analyzing heartbeat signal through remote photoplethysmography (rPPG).
  • To precisely identify the heartbeat vestige from the observed noisy rPPG signals under practical lighting conditions, A rPPG correspondence feature for 3D mask face anti-spoofing is proposed.

Multi-adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection
Staff Prof. YUEN, Pong Chi
Objectives
  • This work focuses on improving the generalization ability of face anti-spoofing methods from the perspective of the domain generalization.
  • Learning a generalized feature space via a novel multi-adversarial discriminative deep domain generalization framework.

Towards Practical Object Trackers: From Feature Combination to Modality Fusion
Staff Prof. YUEN, Pong Chi
Objectives
  • Developing robust visual tracking and object re-detection framework based on information fusion techniques to handle large appearance variations and tracking loss
  • Developing feature combination models which can adaptively combine appropriate visual features
  • Developing modality fusion models which can exploit the complementarity of other non-visual modalities (data sources) for appearance modeling

Unsupervised Embedding Learning via Invariant and Spreading Instance Feature
Staff Prof. YUEN, Pong Chi
Objectives Learning a discriminative feature extraction Deep Neural Network (DNN) with large-scale unlabeled images, such that the visually similar samples are close to each other in the learned embedding space

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Department of Computer Science, Hong Kong Baptist University
Hong Kong Baptist University