Automatic clusters to face recognition

Anderson Rodrigo dos Santos, Adilson Gonzaga

Abstract. 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 pose dealing with N different individual given under M different view/ poses and illumination. We construct an automatic algorithm that computes and finds clusters within the training group preserving intrinsic human face characteristics. The algorithm named K-PCA applies a SOM neural network in the cluster stage and applies the PCA method into each cluster, that is, each cluster forms an eigenface.

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