Comparison of Tensor Unfolding Variants for 2DPCA-Based Color Facial Portraits Recognition
The paper presents a problem of recognition of color facial images in the aspect of dimensionality reduction performed by means of Principal Component Analysis employing different variants of input data organization. Here, input images are represented by tensors of 3rd order and the PCA is applied for matrices derived from such tensors. Its main advantage is associated with efficient representation of images leading to the accurate recognition. The paper describes practical aspects of the algorithm and its implementation for three variants of tensor unfolding. Furthermore the impact of the number of training/testing images, the reduction ratio and the color model on the recognition accuracy is investigated. The experiments performed on Essex facial image databases showed that face recognition using this type of feature space dimensionality reduction is particularly convenient and efficient, giving high recognition performance.
KeywordsFace Recognition Training Image Recognition Accuracy Karhunen Loeve Transform Classical Principal Component Analysis
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