Face Recognition using Local Quantized Patterns

Sibt Ul Hussain (1) , Thibault Napoléon (1) , Frédéric Jurie (1)

1 Equipe Image - Laboratoire GREYC - UMR6072

This paper proposes a novel face representation based on Local Quantized Patterns (LQP). LQP is a generalization of local pattern features that makes use of vector quantization and lookup table to let local pattern features have many more pixels and/or quantization levels without sacrificing simplicity and computational efficiency. Our new LQP face representation not only outperforms any other representation on challenging face datasets but performs equally well in the intensity space and orientation space (obtained by applying gradient or Gabor Filters) and hence is intrinsically robust to illumination variations. Extensive experiments on several challenging face recognition datasets (such as FERET and LFW) show that this representation gives state-of-the-art performance (improving the earlier state-of-the-art by around 3%) without requiring neither a metric learning stage nor a costly labelled training dataset, having the comparison of two faces being made by simply computing the Cosine similarity between their LQP representations in a projected space.

Domaines: Traitement des images [eess.IV]; Apprentissage [cs.LG]

Sibt Ul Hussain, Thibault Napoléon, Frédéric Jurie. Face Recognition using Local Quantized Patterns. British Machive Vision Conference, Sep 2012, Guildford, United Kingdom. 11 p. ⟨hal-00806104⟩