ML | Face Recognition Using Eigenfaces (PCA Algorithm)
In 1991, Turk and Pentland suggested an approach to face recognition that uses dimensionality reduction and linear algebra concepts to recognize faces. This approach is computationally less expensive and easy to implement and thus used in various applications at that time such as handwritten recognition, lip-reading, medical image analysis, etc.
PCA (Principal Component Analysis) is a dimensionality reduction technique that was proposed by Pearson in 1901. It uses Eigenvalues and EigenVectors to reduce dimensionality and project a training sample/data on small feature space. Let’s look at the algorithm in more detail (in a face recognition perspective).
Testing/Detection Algorithm :
Test Images With true labels
- Easy to implement and computationally less expensive.
- No knowledge (such as facial feature) of the image required (except id).
- Proper centered face is required for training/testing.
- The algorithm is sensitive to lightining, shadows and also scale of face in the image .
- Front view of the face is required for this algorithm to work properly.
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