I am a lecturer and graduate student of the Faculty of Information Technology, Multimedia University (Cyberjaya campus) and I am currently part of both Graphics & Vision SIG and Natural Computation and Computational Intelligence (NCCI) SIG.
My research interest is in computer vision, pattern recognition, machine learning and human visual perception, dealing particularly with face recognition, human motion analysis and biometrics in video. Oddly, I also dabble a lot with web scripting and standards - XHTML, AJAX, JSON and CSS.
I graduated with a B.Eng in Electronics in 2002 and Master of Engineering Science (MEngSc) in 2005 from Multimedia University. I am currently working under Dr. Subhas Hati on my Ph.D. in continuous video stream-based face recognition.
Teaching
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My consultation hours are on Thursdays: 11am-1pm, 4pm-6pm
All other meetings are by appointment (unless pre-arranged/fixed)
FYP
There are 2 projects available for Final Year undergraduates next year. Please see me if you're interested to know more about these topics:
- Squadron Assault: Simulating Tactical Bots - Ahsan Raza Sheikh & Yi Shao
- Map Mashups with a Malaysian Flavour - Nik Muhammad Izwan & Mohammad Shahman
- Video Object Segmentation for Scene Understanding [Available]
- Making Computers Watch Football [Available]
* indicates student-proposed projects
For more details on the above projects, please refer to the FYP site.
Research
Continuous video stream-based face recognition
Conventional face recognition research places much emphasis on still images and have shown to achieve a good level of success in mostly restrictive conditions such as near-frontal poses, controlled lighting and rigid face motions. However, recent significant interest in video-based face recognition has earmarked it as a key emerging topic. This research aims to present new approaches for the recognition of faces in continuous video stream. These approaches will utilise visual dynamics and spatio-temporal information for the tracking and recognition of human faces in video sequences. As video sequences have the luxury of having both spatial and temporal information, directions of this research will focus on manifold representation (spatial), temporal information extraction (temporal), and unification under a probabilistic framework.
Collaborators: Dr. Subhas Hati & Dr. Andrew Teoh
FACEFIND: Detecting faces in groups of people
Although face detection is a fairly simple task to the human eye, performing the similar task on a machine can be challenging. In a group of people, partial occlusion and passing objects are often encountered . The FACEFIND project aims to design a multiple-person face detector, which is able to reliably "find" all faces in a group of people. In Part I, a novel RGB-Y-CbCr skin colour model was designed to extract multiple faces in colour images.
Collaborators: Nusirwan Anwar, Kit Chong Wei
Fuzzy-based Gaussian edge detector
Many edge detection schemes suffer from the lack of image quality at the global level. Global properties are more vital in grayscale images due to loss of hue and texture. This work proposes a novel fuzzy-based Gaussian edge detector that uses both global and local image properties for grayscale images.
Collaborators: Prof. Madasu Hanmandlu
Face recognition using feature fusion with multi-tier classification
Face recognition algorihtms have much developed over the years. In the light of present advances processing power, there is much interest in assimilating more features and classifiers together to improve the robustness and accuracy of algorithms. Here, a face recognition algorithm which incorporates a feature fusion method with a multi-tier classification scheme is proposed.
Collaborators: Dr. Lee Sze Wei
Human motion detection using fuzzy rule-base classification of moving blobs
The task of detecting and classifying human motion is an important preliminary tool for many high-level applications. However, the lack of robust classification and proper motion cues is a common problem in many approaches. In this work, a novel human motion detection algorithm that uses a fuzzy rule-base classification scheme based on moving blob regions is proposed.
Publications
Journal papers
- An Integrated Vision-based Architecture for Home Security System
John See, Lee Sze Wei.
IEEE Transactions on Consumer Electronics, 53(2), pp. 489-498, May 2007.
Conference proceedings (sorted by latest)
- RGB-H-CbCr Skin Colour Model for Human Face Detection
Nusirwan Anwar Abdul Rahim, Kit Chong Wei, John See.
MMU International Symposium on Information & Communications Technologies (M2USIC 2006), PJ, Malaysia, 2006.
- Human Motion Detection Using Fuzzy Rule-base Classification of Moving Blob Regions
John See, Lee Sze Wei, Madasu Hanmandlu.
International Conference on Robotics, Vision, Information and Signal Processing (ROVISP 2005), Penang, Malaysia, pp. 398-402, 2005.
- Fuzzy-based Parameterized Gaussian Edge Detector Using Global and Local Properties
John See, Madasu Hanmandlu, Shantaram Vasikarla.
International Conference on Information Technology: Coding and Computing (ITCC'05), Las Vegas, USA, pp. 101-106, 2005.
- Fuzzy Edge Detector Using Entropy Optimization
Madasu Hanmandlu, John See, Shantaram Vasikarla.
International Conference on Information Technology: Coding and Computing (ITCC'04), Las Vegas, USA, pp. 636-642, 2004.
Dissertations
- Algorithms For An Integrated Vision-Based Home Security System
John See. MEngSc thesis, Faculty of Engineering, Multimedia University. April 2005.
- Lossy Compression Techniques In Medical Imaging
John See. Undergraduate final year project thesis, Faculty of Engineering, Multimedia University. March 2002.
