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Advances in data collection and storage capabilities during the past decades have led to an information overload in most sciences. Researchers working in domains as diverse as engineering, astronomy, biology, remote sensing, economics, and consumer transactions, face larger and larger observations and simulations on a daily basis. Such datasets, in contrast with smaller, more traditional datasets that have been studied extensively in the past, present new challenges in data analysis. Traditional statistical methods break down partly because of the increase in the number of observations, but mostly because of the increase in the number of variables associated with each observation. The dimension of the data is the number of variables that are measured on each observation. High-dimensional datasets present many mathematical challenges as well as some opportunities, and are bound to give rise to new theoretical developments. One of the problems with high-dimensional datasets is that, in many cases, not all the measured variables are "important" for understanding the underlying phenomena of interest. While certain computationally expensive novel methods can construct predictive models with high accuracy from high-dimensional data, it is still of interest in many applications to reduce the dimension of the original data prior to any modeling of the data. In mathematical terms, the problem can be stated as follows: given the random variable p-dimensional x = (x1,..., xp), find a lower dimensional representation of it, s = (s1,..., sk) with k<=p, that captures the content in the original data, according to some criterion. The components of s are sometimes called the hidden components. Different fields use different names for the the term "variable" is mostly used in statistics, while "feature" and "attribute" are alternatives commonly used in the computer science and machine learning literature. We have developed an algorithm for face recognition based on Hierachical Dimensionality Reduction: we show that the proposed method is an efficient way of representing face patterns as well as reducing dimension of multidimensional feature.

Index Terms: Matlab, source, code, face, recognition, matching, hierarchical, dimensionality, dimensionality, reduction.

 

 

 

 

Figure 1. Hierarchical clustering diagram



A simple and effective source code for Face Recognition Based on Hierarchical Dimensionality Reduction.

Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox is required.

Release
Date
Major features
1.0

2009.03.28



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The authors have no relationship or partnership with The Mathworks. All the code provided is written in Matlab language (M-files and/or M-functions), with no dll or other protected parts of code (P-files or executables). The code was developed with Matlab 2006a. Matlab Image Processing Toolbox is required. The code provided has to be considered "as is" and it is without any kind of warranty. The authors deny any kind of warranty concerning the code as well as any kind of responsibility for problems and damages which may be caused by the use of the code itself including all parts of the source code.

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