Chengjun Liu

Professor
Department of Computer Science
New Jersey Institute of Technology
Newark, NJ 07102

Email: chengjun.liu@njit.edu
Phone: 973-596-5280
Office: GITC 4306

Research Interests

Pattern Recognition (Face/Iris Recognition, Color Image Feature Extraction and Classification, Classifier Fusion)
Machine Learning (Statistical Learning, Kernel Methods, Innovative Kernel Functions/Models, Similarity Measures)
Computer Vision (Object/Face/Iris/Eye Detection, Motion Analysis and Video Processing)
Image and Video Analysis (Image Search and Retrieval, Image Category Classification, Color Image Analysis, New Color Spaces, Gabor Image Representation)
Security (Biometrics)

Patents

C. Liu: "Face Detection Method and Apparatus", United States Patent 7,162,076, January 9, 2007.
C. Liu and H. Wechsler: "Feature Based Classification", United States Patent 6,826,300, November 30, 2004.

Publications (by category) (by year) (citation in SCOPUS) (citation in Google Scholar)


Teaching


Recent Research

Pattern Recognition, Machine Learning, and Image Processing -- We develop new color models, advanced pattern recognition and machine learning methods, and fuse them to address large-scale and grand-challenge problems, such as the face recognition grand challenge (FRGC) problem and the Caltech 256 image categories image search and classification problem.  
By fusing our new kernel methods (kernel Fisher analysis, kernel PCA with fractional power polynomial models), new color models, new similarity measures, we achieve the best face verification performance for the government organized FRGC competition.
By fusing new color models with popular image descriptors, such as the scale-invariant feature transform (SIFT) and the local binary patterns (LBP), we are able to develop new image descriptors with improved image search and image category classification performance.

Computer Vision -- We develop new statistical methods for more accurate and efficient target detection from image and video.
One example is an efficient support vector machine (eSVM).  The eSVM, which introduces a single value for all the slack variables corresponding to the training samples on the wrong side of their margin, defines a much smaller set of support vectors and hence improves computational efficiency without sacrificing generalization performance. 
Another example is feature local binary patterns (FLBP).  The FLBP method, which encodes both local and feature information, improves upon the popular LBP approach for texture description and pattern recognition.
Yet another example is the Bayesian discriminating features (BDF) method.  The BDF method, when trained on images from only one database yet works on test images from diverse sources, displays robust generalization performance for face detection.
The eSVM, FLBP, and BDF methods have been successfully applied to automatic target detection on large-scale and challenging databases, such as eye detection and face detection.

Biometrics and Security -- We have developed advanced face recognition, face detection, iris detection and recognition, image search, and image category classification technologies for homeland security, justice and law enforcement, and business applications.