Face Perception using Tensor Approach
Principal Component Analysis (PCA) is one of the common statistical techniques that can also be used for face perception. This approach introduces a single two-dimensional representation for facial attributes, allowing only one attribute to be different at a time. While a face consists of a set of...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/23833/1/IEEE_PAPER%20-%20Copy.pdf http://ir.unimas.my/id/eprint/23833/ https://www.researchgate.net/publication/330704191_Face_Perception_using_Tensor_Approach |
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| Summary: | Principal Component Analysis (PCA) is one of the
common statistical techniques that can also be used for face perception. This approach introduces a single two-dimensional representation for facial attributes, allowing only one attribute to be different at a time. While a face consists of a set of edges that will define the shape and positions of facial features, these face
properties may contribute differently and influence the actual face recognition performance. Therefore in this paper, we propose to extend the traditional PCA approach to a multidimensional tensor-based method. This approach could efficiently separate the facial attributes. Experiment was conducted and the obtained results have shown a higher recognition rates compared to the traditional PCA method. |
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