Distance measures for PCA-based face recognition

By Vytautas Perlibakas. Pattern Recognition Letters, 2004

362 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a2ff11632d5116f0510800d84ae2943029f4fd33

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Ying Tan
2. Teh Ying Wah
3. Yuanchun Zhu
4. Andrew R. Dalby
5. Saeed Reza Aghabozorgi
6. Ali Seyed Shirkhorshidi

In Signal Processing

1. Ying Tan
2. Teh Ying Wah
3. Yuanchun Zhu
4. Saeed Reza Aghabozorgi
5. Ali Seyed Shirkhorshidi

Citations in the corpus

• A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data (2015, background): “A study by Perlibakas demonstrated that a modified version of this distance measure is among the best distance measures for PCA-based face recognition [34].”
• Fireworks Algorithm for Optimization (2010, methodology): “When calculating the distance, any distance measure can be utilized including Manhattan distance, Euclidean distance, Angle-based distance, and so on [9].”