Examplers based image fusion features for face recognition
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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 99.5 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.
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