Multiscale Local Phase Quantization for Robust Component-Based Face Recognition Using Kernel Fusion of Multiple Descriptors

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 35; no. 5; pp. 1164 - 1177
• Main Authors: Chan, Chi Ho, Tahir, Muhammad Atif, Kittler, Josef, Pietikainen, Matti
• Format: Journal Article
• Language: English
• Published: Los Alamitos, CA IEEE 01.05.2013; IEEE Computer Society
• Subjects: Algorithms; Applied sciences; Artificial Intelligence; Biometric Identification - methods; Computer science; control theory; systems; Data processing. List processing. Character string processing; Databases, Factual; Discriminant Analysis; Exact sciences and technology; Face; Face - anatomy & histology; face image representation; Face recognition; Histograms; Humans; Image Processing, Computer-Assisted; Kernel; kernel discriminant analysis; kernel fusion; Lighting; local binary pattern; local phase quantization; Memory organisation. Data processing; Pattern recognition. Digital image processing. Computational geometry; Software; Vectors; Image processing; local phase quantization; Electronic countermeasure; Texture; System decomposition; Image matching; local binary pattern; Efficiency; Signal quantization; Classification; Facies; Database; Illumination; Robustness; Combined system; Pattern extraction; Computer vision; Discriminant analysis; Merging sort; kernel discriminant analysis; Face recognition; blurring image; Software component; Blurred image; Computational geometry; Luminance; Multiscale method; kernel fusion; Alignment defect; face image representation
• ISSN: 0162-8828; 1939-3539; 2160-9292; 1939-3539
• DOI: 10.1109/TPAMI.2012.199

Face recognition subject to uncontrolled illumination and blur is challenging. Interestingly, image degradation caused by blurring, often present in real-world imagery, has mostly been overlooked by the face recognition community. Such degradation corrupts face information and affects image alignment, which together negatively impact recognition accuracy. We propose a number of countermeasures designed to achieve system robustness to blurring. First, we propose a novel blur-robust face image descriptor based on Local Phase Quantization (LPQ) and extend it to a multiscale framework (MLPQ) to increase its effectiveness. To maximize 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 normalizations are used to generate and combine multiple scores from different face image scales to further enhance the accuracy. The proposed approach has been comprehensively evaluated using the combined Yale and Extended Yale database B (degraded by artificially induced linear motion blur) as well as the FERET, FRGC 2.0, and LFW databases. The combined system is comparable to 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.