Agrupamento de Pixels para o Reconhecimento de Faces
This addresses face recognition efficiency for computer vision applications, but appears incremental as it builds on existing feature extraction methods.
The researchers tackled the problem of face recognition by proposing Pixel Clustering to define image regions with similar pixels and extract features, achieving high accuracy with a maximum of 512 features and robustness with limited training classes.
This research starts with the observation that face recognition can suffer a low impact from significant image shrinkage. To explain this fact, we proposed the Pixel Clustering methodology. It defines regions in the image in which its pixels are very similar to each other. We extract features from each region. We used three face databases in the experiments. We noticed that 512 is the maximum number of features needed for high accuracy image recognition. The proposed method is also robust, even if only it uses a few classes from the training set.