Unsupervised SIFT-based Face Recognition Using an Automatic Hierarchical Agglomerative Clustering Solution | Zendy

Tudor Barbu | Zendy

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Unsupervised SIFT-based Face Recognition Using an Automatic Hierarchical Agglomerative Clustering Solution

Author(s) -

Tudor Barbu

Publication year - 2013

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2013.09.116

Subject(s) - computer science , artificial intelligence , scale invariant feature transform , pattern recognition (psychology) , hierarchical clustering , face (sociological concept) , cluster analysis , facial recognition system , feature extraction , feature (linguistics) , search engine indexing , feature vector , hierarchical clustering of networks , fuzzy clustering , canopy clustering algorithm , social science , sociology , linguistics , philosophy

In this paper, we propose a robust automatic unsupervised face recognition system using SIFT characteristics. A SIFT- based feature extraction is performed on the analyzed face images. Then, we introduce a novel metric for the obtained feature vectors. Next, we develop an automatic facial feature vector classification technique based on a hierarchical agglomerative clustering algorithm and some validation indexes. The recognition system described here works for large sets of faces and can be successfully applied in the face database indexing domain

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