Automatic Clusters to Face Recognition

Anderson Rodrigo dos SantosAdilson Gonzaga

In this paper we consider to study the distribution of the vectors of face in the dimensional space(n x m pixels of the image), and we have developed a face recognition that works under varying posedealing with N different individual given under M different view/ poses and illumination. We constructan automatic algorithm that computes and finds clusters within the training group preserving intrinsichuman face characteristics. The algorithm named K-PCA applies a SOM neural network in thecluster stage and applies the PCA method into each cluster, that is, each cluster forms an eigenface.The recognition rate using the data base Umist and Essex are 85% and 99%.

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