Face Recognition Using Local Gabor Phase Characteristics

Jiang, Yanxia; Ren, Bo

doi:10.1109/cise.2010.5677191

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…For this reason, we have used the Yale [32], ORL [33], UMIST [34], AR [35] and Indian [36] face databases. Moreover, for the same datasets, we have compared the different face recognition methods, using Local Normalization-Local Gabor Binary Pattern (LN-LGBP) descriptor [30] and [31] instead of GOM descriptor for face image representation, in order to show the advantage of the GOM descriptor. A detailed description of the used face databases is represented in Table 4.…”

Section: Contextualized Evaluation and Comparisonmentioning

confidence: 99%

An Adaptive Face Recognition System Based on a Novel Incremental Kernel Nonparametric Discriminant Analysis

Soula¹,

Said²,

Ksantini³

et al. 2019

KSII TIIS

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This paper introduces an adaptive face recognition method based on a Novel Incremental Kernel Nonparametric Discriminant Analysis (IKNDA) that is able to learn through time. More precisely, the IKNDA has the advantage of incrementally reducing data dimension, in a discriminative manner, as new samples are added asynchronously. Thus, it handles dynamic and large data in a better way. In order to perform face recognition effectively, we combine the Gabor features and the ordinal measures to extract the facial features that are coded across local parts, as visual primitives. The variegated ordinal measures are extraught from Gabor filtering responses. Then, the histogram of these primitives, across a variety of facial zones, is intermingled to procure a feature vector. This latter's dimension is slimmed down using PCA. Finally, the latter is treated as a facial vector input for the advanced IKNDA. A comparative evaluation of the IKNDA is performed for face recognition, besides, for other classification endeavors, in a decontextualized evaluation schemes. In such a scheme, we compare the IKNDA model to some relevant state-of-the-art incremental and batch discriminant models. Experimental results show that the IKNDA outperforms these discriminant models and is better tool to improve face recognition performance.

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Section: Contextualized Evaluation and Comparisonmentioning

confidence: 99%

An Adaptive Face Recognition System Based on a Novel Incremental Kernel Nonparametric Discriminant Analysis

Soula¹,

Said²,

Ksantini³

et al. 2019

KSII TIIS

View full text Add to dashboard Cite

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A Monogenic Local Gabor Binary Pattern for Facial Expression Recognition

Eleyan

Ashir

2017

Scitech

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ABSTRACT:The paper implements a monogenic-Local Binary Pattern (mono-LBP) algorithm on Local Gabor Pattern (LGP). The proposed approach initially features from the samples using LGP at different scales and orientation. The extracted LGP features are further enhanced by decomposing it into three monogenic LBP channels before being recombined to generate the final feature vector. Different Normalization schemes are applied to the final feature vector. Two best performing normalization algorithms with mono-LBP are fused at score level to obtain an improved performance using K-Nearest Neighbor classifier with L1-norm as a distance metrics. Moreover, performance comparison is done with other variants of LGP algorithm and also the effects of various normalization techniques are investigated. Experimental results from JAFFE and TFEID facial expression databases show that the new technique has improved performance compared to its counterparts. Key Words: Facial expression recognition, Local gabor patterns, Monogenic local binary patterns. Yüz İfade Tanınması İçin Bir Monojenik Yerel Gabor İkili DesenÖZ: Bu makale, yerel Gabor Desen (LGP) üzerinde monojenik-Yerel İkili Desen (mono-LBP) algoritmasını uygular. Önerilen algoritma Gabor çekirdeğinin farklı ölçeklerinde ve farklı normalizasyon şemaları ile uygulanır. Mono-LBP ile en iyi performans gösteren normalleştirme algoritmalarından elde edilen sonuçlar, geliştirilmiş bir performans elde etmek için skor düzeyinde birleştirilmiştir. Üstelik, performans karşılaştırması diğer LGP algoritmasının türevleri ile yapılmıştır ve ayrıca çeşitli normalleştirme tekniklerinin etkileri araştırılmaktadır. JAFFE yüz ifadesi veritabanında yapılan deneysel sonuçlarine göre, önerilen yaklaşım bir sınıflandırıcı olarak mesafe metrikini kullanarak mevcut algoritmalara kıyasla en iyi ortalama performansa sahip olduğunu göstermektedir. Anahtar Kelimeler: Yüz ifade tanıma, Yerel gabor desen, Monojenik yerel ikili desen.

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Face Recognition Using Local Gabor Phase Characteristics

Cited by 2 publications

References 9 publications

An Adaptive Face Recognition System Based on a Novel Incremental Kernel Nonparametric Discriminant Analysis

An Adaptive Face Recognition System Based on a Novel Incremental Kernel Nonparametric Discriminant Analysis

A Monogenic Local Gabor Binary Pattern for Facial Expression Recognition

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