A novel face recognition system base on nonparametric discriminant analysis

Soula, Arbia; Ksantini, Riadh; Said, Salma Ben; Lachiri, Zied

doi:10.1109/cadiag.2017.8075682

Cited by 1 publication

(1 citation statement)

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“…Recently, Arbia Soula et al presented in [7] and [8] a novel face recognition systems formulated on Gabor and ordinal filters for feature extrication, and on the Kernel Fisher Discriminant Analysis (KFD) and the Kernel Nonparametric Discriminant Analysis (KNDA), respectively, for dimension reduction and classification. In fact, both dimension reduction and classification methods are grounded on flexible non-linear facial classes' disjunction.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%