Multiscale Local Phase Quantisation for Robust Component-based Face Recognition using Kernel Fusion of Multiple Descriptors

Chi-Ho Chan, Muhammad Tahir, Josef Kittler, Matti Pietikainen

Research output: Contribution to journalArticlepeer-review

133 Citations (Scopus)

Abstract

Face recognition subject to uncontrolled illumination and blur is challenging. Interestingly, image degradation caused by blurring has mostly been overlooked by the face recognition community. We propose a number of counter-measures designed to achieve system robustness to blurring. First, we propose a novel blur-robust face image descriptor based on Local Phase Quantisation (LPQ) and extend it to a multiscale framework (MLPQ) to increase its effectiveness. To maximise the insensitivity to misalignment, the MLPQ descriptor is computed regionally, by adopting a component-based framework. Second, the regional features are combined using kernel fusion. Third, the proposed MLPQ representation is combined with the Multiscale Local Binary Pattern (MLBP) descriptor using kernel fusion to increase insensitivity to illumination. Kernel Discriminant Analysis (KDA) of the combined features extracts discriminative information for face recognition. Last, two geometric normalisations are used to generate and combine multiple scores from different face image scales to enhance the accuracy further. The proposed approach has been comprehensively evaluated using several well-known databases. The combined system is comparable to the state of the art approaches using similar system configurations. The reported work provides a new insight into the merits of various face representation and fusion methods, as well as their role in dealing with variable lighting and blur degradation.
Original languageEnglish
Pages (from-to)1-1
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume99
DOIs
Publication statusPublished - 2012

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