Eigenfeature Optimization for Face Detection
نویسندگان
چکیده
PCA is a well-know dimension reduction methodology that can also be employed for face detection task. Although the use of eigenface as the basis for face detection or recognition under PCA-based regime is so generalized, little is known about its characteristics. We study its feature by data visualization in the face space of varying dimension and the comparison of the face detection rate in differing number and order of eigenfaces. Our experimental result shows that there are certain eigenfeatures that could serve greatly for the detection performance and the flexible adoption of weighting can improve the performance even more.
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