MITACS Globalink– Faculty Project Overview
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Please note: For those faculty projects selected in the Globalink competition, all information on this form
will be posted publicly on the MITACS Globalink website for the purposes of student applications.
Faculty Member Name: Tien D. Bui
Department: Computer Science and Software Engineering
University: Concordia
Language of supervision for the research project: X English
 French
Project Title:
AUTOMATIC FACIAL FEATURES DETECTION SYSTEM
Brief Project Overview: (provide a brief description of no more than 1-2 pages of the research
project for which you are seeking a Globalink student)
I. Project Description
Facial Features Detection (FFD) is a fundamental step and plays a very important role in
numerous face related applications, such as: face recognition, facial expression
recognition, face age-estimation, etc. However, most FFD systems are currently able to
detect only frontal and neutral face images in a very good lighting condition that is
usually difficult to achieve in practice. Therefore, to set up a real-time FFD system with
high-accuracy we still meet many challenges due to the complexity of facial expressions
and lighting sources.
This project is to develop an automatic high-accuracy FFD system that has ability to
deal with emotional faces and in different lighting conditions. This system has ability to
automatically detect 68, 86 or 120 landmark points from an input 2-D face image. The
project will be developed in C++ with some other image processing and machine
learning libraries, such as: OpenCV, LibSVM, Wavelet library, etc.
II. Methodologies
MITACS Globalink– Faculty Project Overview
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There are many approaches to solve this problem, such as: Markov Random Fields
(MRFs), Active Contour Models (snakes), etc. Apart from these methods, an effective
statistical model to extract facial features called Active Shape Model (ASM) [1] has been
developed and extended [2-10]. ASM is based on a statistical shape model that describes
both typical shape and typical variability, and is quite similar to Active Contour
Models. The fundamental difference is that ASM allows the shape model to deform
only in ways that are consistent with the shapes in the training set. There are two main
stages to develop an ASM: building shape model and image search.
However, the basic ASM also gives incorrect detection results when input images are
not frontal faces or have noise and expressions. Therefore, we propose an improved
method by combining ASM with wavelet techniques that have ability to deal with
different lighting conditions and facial expressions. Additionally, the set L-1
optimization algorithms are used to improve the searching results.
[1] T. F. Cootes, D. Cooper, C. J. Taylor and J. Graham, “Active Shape Models - Their
Training and Application,” Computer Vision and Image Understanding, 1995.
[2] Keshav Seshadri and Marios Savvides, “Robust Modified Active Shape Model for
Automatic Facial Landmark Annotation of Frontal Faces”, BTAS 2009.
[3] S. Milborrow and F. Nicolls, “Locating facial features with an extended active shape
model,” ECCV, 2008. http://www.milbo.users.sonic.net/stasm/
[4] T. F. Cootes, G. J. Edwards, and C. J. Taylor, “Comparing active shape models with
active appearance models,” in Proc. British Machine Vision Conf., 1999, pp. 173–182.
[5] G. Hamarneh and T. Gustavsson, “Deformable spatio-temporal shape models:
Extending ASM to 2D + Time,” in Proc. 12th Br. Machine Vision Conf., 2001, pp. 13–22.
[6] Y. Fu, W. Gao, Y. Xiao and J. Liu, “A framework for automatic construction of 3D
PDM from segmented volumetric neuroradiological data sets,” Computer Methods and
Programs in Biomedicine, Vol. 97, Issue 3, Pages 199-210, March 2010.
[7] Li Zhang, Haizhou Ai, “Multi-View Active Shape Model with Robust Parameter
Estimation,” International Conference on Pattern Recognition (ICPR'06), 2006.
MITACS Globalink– Faculty Project Overview
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[8] M. Mahoor and M. Mottaleb, “Facial Features Extraction in Color Images Using
Enhanced Active Shape Model,” in FGR, pages 144– 148, 2006.
[9] C. Davatzikos, X. Tao, and D. Shen, “Hierarchical active shape models, using the
wavelet transform,” IEEE Trans. Med. Imaging, vol. 22, pp.414–423.
[10] F. M. Sukno, S. Ordas, C. Butakoff, S. Cruz, “Active shape models with invariant
optimal features: Application to facial analysis,” IEEE Trans. PAMI, 29(7), 1105–1117,
2007.
Role for Student: (provide a brief description of no more than 1 page the specific role
that you have for the Globalink student on the research project)
The student will be involved in the following two steps. However, step 1 will be the
responsibility of a doctoral student. The Globalink student will be working mainly on
step 2:
1. An advanced method for Facial Features Detection with high-accuracy. The
method has to qualify for the following characteristics:
o Real-time application
o Work in different lighting conditions
o Detect features from input faces with expressions.
2. A FFD software based on the proposed method.
The student will be working very closely with a doctoral student to develop the
software described in the project description.
MITACS Globalink– Faculty Project Overview
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Student Background / Skills Requirements: (specify research interest requirements,
knowledge background required, prior course prerequisites if any, software or
hardware knowledge if any, other specialized skill sets needed)
The student must have some background on Principal Component Analysis method,
good C++, and Matlab programming ability, good mathematical skill, and some basic
image processing knowledge. Prior courses in these areas are required.
Additional Comments / Requirements: (specify any additional comments or
requirements that might be helpful for students to evaluate the research project and
their role).
The student is expected to have done courses in linear algebra, statistics and probability
theory, numerical methods, advanced programming methodology, image processing,
pattern recognition.