Revisiting the performance of PCA versus FDA versus Simple Projection for image recognition
1 Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX-79409, USA.
2 Department of Mathematical Sciences, The University of Texas at El Paso, El Paso, TX-79968, USA.
Research Article
Global Journal of Engineering and Technology Advances, 2021, 08(01), 085-095.
Article DOI: 10.30574/gjeta.2021.8.1.0099
Publication history:
Received on 02 June 2021; revised on 11 July 2021; accepted on 13 July 2021
Abstract:
In this paper, our main aim is to show a better dimension reduction process of high dimensional image data sets from several existing techniques. To verify it we start with most useful singular value decomposition to reduce the dimensionality of data to incorporate principal components. On the other hand, we classify data in advance to work out Fisher’s discriminant. From many real-world examples, we set a very well-known paradigm of analysis using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) or Fisher Discriminant Analysis (FDA) and Simple Projection (SP) to recognize people from their facial images. We consider that we have some images of known people that can be used to compare and recognize new images (of the same set of face images). Moreover, we show graphical and tabular representation for average performance of correct recognition as well as analyze the effectiveness of three different machine learning techniques.
Keywords:
SVD; Orthogonal linear transformation; Orthogonal projection; PCA; LDA
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