Unsupervised Sub-graph Selection and Its Application in Face Recognition Techniques

  • Ahmed ElSayedEmail author
  • Ausif Mahmood
  • Tarek Sobh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9164)


One of the limitations of the existing face recognition algorithms is that the recognition rate significantly decreases with the increase in dataset size. In order to eliminate this shortcoming, this paper presents a new training dataset partitioning methodology to improve face recognition for large datasets. This methodology is then applied to the Eigenface algorithm as one of the algorithms that suffer from this problem. The algorithm represents the training face images as a fully connected graph. This graph is then divided into simpler sub-graphs to enhance the overall recognition rate. The sub-graphs are generated dynamically, and a comparison between different sub-graph selection techniques including minimizing edge weight sums, random selection, and maximizing sum of edge weights inside the sub-graph are provided. It is concluded that the optimized hierarchical dynamic technique increased the recognition rate by more than 40 percent in a large benchmark image dataset compared to the original single large graph method. Furthermore, the developed technique is compatible with several other unsupervised face recognition techniques such as ICA, KPCA, RBM, SIFT, and LBP... etc., and other datasets, specially if the number of images per person in the training data are low.


Sub-graph selection Graph theory Hierarchical recognition Face recognition 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity of BridgeportBridgeportUSA

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