Examplers based image fusion features for face recognition
This is an incremental improvement for face recognition systems, enhancing accuracy and stability over single-model approaches.
The paper tackles face recognition by using examplers formed from multiple gallery images per person, achieving high recognition accuracies such as 99.0% on AR and 100.0% on YALE databases.
Examplers of a face are formed from multiple gallery images of a person and are used in the process of classification of a test image. We incorporate such examplers in forming a biologically inspired local binary decisions on similarity based face recognition method. As opposed to single model approaches such as face averages the exampler based approach results in higher recognition accu- racies and stability. Using multiple training samples per person, the method shows the following recognition accuracies: 99.0% on AR, 99.5% on FERET, 99.5% on ORL, 99.3% on EYALE, 100.0% on YALE and 100.0% on CALTECH face databases. In addition to face recognition, the method also detects the natural variability in the face images which can find application in automatic tagging of face images.