A Pruning Approach Improving Face Identification Systems

Anis Chaari (1) , Sylvie Lelandais (1) , Mohamed Ben Ahmed (2)

1 IBISC - Informatique, Biologie Intégrative et Systèmes Complexes
2 RIADI - Laboratoire de recherche en Génie Logiciel, Applications distribuées, Systèmes décisionnels et Imagerie intelligente [Manouba]

We propose, in this paper, a new biometric identification approach which aims to improve recognition performances in identification systems. We aim to split the identity database into well separated partitions in order to simplify the identification task. In this paper we develop a face identification system and we use the reference algorithms of eigenfaces and fisherfaces in order to extract different features describing each identity. These features, which describe faces, are generally optimized to establish the required identity in a classical identification process. In this work, we develop a novel criterion to extract features used to partition the identity database. We develop database partitioning with clustering methods which split the gallery by bringing together identities which have similar features and separating dissimilar features in different bins. Pruning the most dissimilar bins from the query identity features allows us to improve the identification performances. We report results from the XM2VTS database.

Mots clés: biometry; face identification; clustering; feature extraction; image database

Domaines: Sciences de l'ingénieur [physics]

Anis Chaari, Sylvie Lelandais, Mohamed Ben Ahmed. A Pruning Approach Improving Face Identification Systems. Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2009), Sep 2009, Genoa, Italy. pp.85-90, ⟨10.1109/AVSS.2009.80⟩. ⟨hal-00653989⟩