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Face Recognition using Local Quantized Patterns (2012)
| Citations: | 31 - 2 self |
BibTeX
@MISC{Hussain12facerecognition,
author = {Sibt Ul Hussain and Thibault Napoléon and Fréderic Jurie},
title = { Face Recognition using Local Quantized Patterns},
year = {2012}
}
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Abstract
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.
Keyphrases
local quantized pattern face recognition local pattern feature computational efficiency new lqp face representation projected space novel face representation metric learning stage face datasets lqp representation state-of-the-art performance lookup table vector quantization gabor filter quantization level extensive experiment face recognition datasets orientation space intensity space costly labelled training dataset cosine similarity
