Thursday, January 19, 2017

9:09 PM

The Principal Component Analysis (PCA) is one of the most successful techniques that have been used in image recognition and compression. PCA is a statistical method under the broad title of factor analysis. The purpose of PCA is to reduce the large dimensionality of the data space (observed variables) to the smaller intrinsic dimensionality of feature space (independent variables), which are needed to describe the data economically. This is the case when there is a strong correlation between observed variables.

The jobs which PCA can do are prediction, redundancy removal, feature extraction, data compression, etc. Because PCA is a classical technique which can do something in the linear domain, applications having linear models are suitable, such as signal processing, image processing, system and control theory, communications, etc.

Face recognition has many applicable areas. Moreover, it can be categorized into face identification, face classification, or sex determination. The most useful applications contain crowd surveillance, video content indexing, personal identification (ex. driver’s licence), mug shots matching, Entrance security, etc. The main idea of using PCA for face recognition is to express the large 1-D vector of pixels constructed from 2-D facial image into the compact principal components of the feature space. This can be called eigenspace projection. Eigenspace is calculated by identifying the eigenvectors of the covariance matrix derived from a set of facial images(vectors). The details are described in the following section.

Many method can implement to our project, but for this session I use PCA (Principal Component Analysis) to understanding the pattern of my face. before our learning can recognition our face, firsly we must take the samples of our gesture like figure 1.

Figure 1. Face Database

If you want to know more about how PCE algoritm until can recognize the face please visit my older post in Here. If you want to download full project source code you can download in button below. My program run in Visual Studio 2013 if you haven't yet you can download the software HERE. for library I use EmguCV 2.4.10

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