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Dr. AJMAL S. MIAN's Home Page

PhD Scholarships: I have PhD scholarships for outstanding students. You can apply now or in the main IPRS scholarship rounds [download PDF flyer]
[tags: PhD scholarship, living allowance, multispectral, hyperspectral, image set classification, video analysis, range image]

NEWS:
1. Part time Research Assistant position available. Skills required = OpenCV or Matlab, tracking and stereo reconstruction or feature extraction and classification. 
Send me an email with your CV (local applicants only). 
2. We will soon advertise two new postdoctoral/research fellow positions.

Google Scholar Citations

Research

	Sparse Approximated Nearest Points for Image Set Classification Image sets are represented by the image samples and their affine hull model. The dissimilarity between sets is measured as the distance between their nearest points that can be sparsely approximated from the sample images of the respective set.		Robust Realtime Feature Detection in Raw 3D Face Images We avoid preprocessing 3D data for noise/spike removal and hole filling and detect the outer eye corners and nose tip with over 99% accuracy using Haar features extracted from gradient of range images. [download code/haarcascades]
	Desktop Optics for Face Recognition and 3D Reconstruction Facial features are extracted at multiple scales and orientations and projected separately to linear subspaces for illumination invariant recognition. A model based approach and Support Vector Regression is used to reconstruct 3D faces.		Detection and Recognition of 3D Ears Haar features are used to detect and extract ears regions. Keypoints are then localized on the 3D ear surface for extracting local features. Features are matched using various geometric constraints for person identification.
	Shade Face: Muliple Image-based face recognition A global signature of the face, under varying illumination, is constructed using contourlet coefficients. Illumination is varied using a computer screen. Face signatures are matched using sliding windows and the results are compared to 3D face recognition.		3D Scale Invariant Keypoints Multi-scale keypoints are detected on 3D objects/ partial surfaces for pose and scale invariant object retrieval from cluttered scenes. Features are ranked using a quality measure and the best scale for feature (at a keypoint) is automatically determined from data.
	Smart Surveillance Realtime face tracking is done with a single PTZ camera. See demos and code for controlling Sony PTZ network camera.		Video-based Face Recognition Unsupervised learning is performed from local features for video-based face recognition. An extension of this technique performs online learning (with near realtime feedback) and face recognition.
	3D Multimodal hybrid face recognition This research has focused on automatic pose correction, keypoint identification, local feature extraction and a region-based matching for robust 3D face recognition.		Keypoint based 3D Face Recognition Unique keypoints are detected on 3D faces to extract invariant features that are projected to the PCA subspace and matched. Similarity between faces is measured using graphs (meshes) constructed from the matching keypoints on individual faces.
	3D object recognition in cluttered scenes An algorithm is prposed for 3D object recognition in cluttered scenes and its performance is compared to the spim images on the same database. The database can be downloaded for comparisons.		Non-Rigid 3D Face Recognition Expression deformations are modelled in PCA subspace and the model is used to morph out expressions from 3D faces. The resulting faces are used for expression invariant 3D face recognition.
	3D modeling A representation for 2.5D surfaces is proposed based on third order tensors. Tensors are used to registere unordered views of an object to construct its complete 3D model. Data is available for comparisons.		Facial Expression Classification An unseen 3D face, under any facial expression, is decomposed into an estimated 3D neutral face and expression deformations. This decomposition classifies the facial expression and improves the accuracy of3D face recognition.
	Internet key exchange A new protocol (Arcanum) has been proposed for secure key exchange over the Internet. Arcanum is secure, robust to DoS attacks and more efficient in terms of CPU consumption and network traffic. Java code can be downloaed.		Integrating Local and Global Features for 3D Face Recognition Local and global geometrical cues are integrated into in a single compact representation for 3D face recognition. The global cues provide geometrical coherence for the local cues resulting in better descriptiveness.

Research Grants
Euan Harvey, Mark Shortis, Ajmal Mian, Philip Culverhouse, Duane Edgington, Danelle Cline, "Automation of Species Recognition and Size Measurement of Fish from Underwater Stereo-video Imagery",
[Collaborators: Australian Fisheries Management Authority, Dept. of Environment and Conservation, Dept. of Environment and Natural Resources, NSW Office of Environment and Heritage, Parks Victoria, SeaGIS Pty. Ltd] ARC Linkage Grant (2011-2014) $436,000\- + Partner contribution

Ajmal Mian, "Active Multispectral Computer Vision for Defense and Security", ARC Discovery Grant (2011-2015) $734,000/-

Robyn Owens, Ajmal Mian, "Person Identification using Multiple Non-invasive Iris and Face Biometrics in Video", ARC Discovery Grant (2010-2012) $390,000\-

Ajmal Mian, "Integration of Spatio-temporal Video Data for Realtime Smart Proactive Surveillance", ARC Discovery Grant (2008-2010) $311,298\-

Ajmal Mian, "Automatic Construction of 3D Deformable Fish Models for Biomass Measurement and Identification", UWA Research Development Award (2010) $28,087\-

Ajmal Mian, "Fusion of Physiological and Behavioural Biometrics for Foolproof User Authentication", UWA Research Grant (2007) $27,000\-

Awards
Outstanding Young Investigator Award, 2011
Australian Research Fellowship (ARF), 2011
Australian Academy of Science ARC Award, 2009
Postdoc Award for Best Work Accepted for Publication, 2009
Australian Postdoctoral Fellowship (APD), 2008
Prize for Higher Degree by Research Achievements, 2007
Australasian Distinguished Doctoral Dissertation Award, 2006 (thesis)
Special Commendation for Higher Degree by Research Achievements, 2006
Postgraduate Research Travel Award, 2005

Past PhD Students
Faisal R. Al-Osaimi (completed in 2010)

Current PhD Students
Zohaib Khan (since June 2010)

Conference Committee Member
ECCV Workshop on Reconstruction and Modeling of Large-Scale 3D Virtual Environments 2010.
Int'l Conf. on Pattern Recognition (ICPR) 2010.
Int’l Conf. on Imaging for Crime Detection and Prevention (ICDP) 2009.
Int'l Workshop on Representation and Modeling of Large-scale 3D Environments (Modeling-3D), 2009.
Int'l Conf. on Image and Vision Computing New Zealand (IVCNZ), 2009, 2010, 2011.
Int'l Conf. on Computer Vision Theory and Applications (VISAPP),  2006, 2007, 2008 and 2009.
Int'l Conf. on Machine Vision (ICMV), 2007
British Machine Vision Conference (BMVC), 2007
Int'l Conf. on Signal Processing and Multimedia Applications (SIGMAP), 2006 and 2007

I review papers for the following journals
IEEE Trans. on Pattern Anaylsis and Machine Intelligence (TPAMI)
Int'l Journal of Computer Vision (IJCV)
IEEE Trans. on Circuits and Systems for Video Technology (TCSVT)
IEEE Trans. on Image Processing
IEEE Trans. on Information Forensics and Security
Image and Vision Computing (Elsevier)
IEEE Trans. on Information Theory (IEEE IT)
Pattern Recognition Letters (Elsevier)
IEEE Trans. on Systems Man and Cybernetics B (SMC)
Signal Processing Journal (Elsevier)
Assembly Automation, Industrial Robot , Sensor Review (Emerald Group Publishing Ltd.)

I have reviewed papers for the following conferences
IVCNZ (2005, 2006, 2008), Australian Joint Conference on AI (2005, 2006), BMVC (2006), ISSPA (2005), 3DIM (2005), IEEE WACV (2005) and Int'l Conf. on Dependable Systems and Networks (DSN 2004).