Determination of the most representative descriptor among a set of feature vectors for the same object
This addresses the challenge of handling sparse feature data in computer vision tasks like face recognition, but appears incremental as it builds on existing robust estimation techniques.
The paper tackles the problem of selecting the most representative feature vector from a set for the same object, using face recognition as an example, by proposing a method based on robust mode-median mixture calculation with a Welsch/Leclerc loss function for sparse feature spaces.
On an example of solution of the face recognition problem the approach for estimation of the most representative descriptor among a set of feature vectors for the same face is considered in present study. The estimation is based on robust calculation of the mode-median mixture vector for the set as the descriptor by means of Welsch/Leclerc loss function application in case of very sparse filling of the feature space with feature vectors