A novel incremental face recognition method based on nonparametric discriminant model

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

doi:10.1109/atsip.2018.8364451

Cited by 5 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1) LDA variations Linear discriminant analysis (LDA) is a method used to find a linear combination of features that characterizes a set of classes. A variation of LDA can be seen in the work of (Soula et al 2018) [17]. In this work, a technique based on Incremental Nonparametric Discriminant Analysis (NDA) is proposed for incremental face recognition.…”

Section: Supervisedmentioning

confidence: 99%

An Efficient Hyperdimensional Computing Paradigm for Face Recognition

2022

View full text Add to dashboard Cite

In this paper, a combined framework is proposed that includes Hyperdimensional (HD) computing, neural networks, and k-means clustering to fulfill a computationally simple incremental learning framework in a facial recognition system. The main advantages of HD computing algorithms are the simple computations needed, the high resistance to noise,and the ability to store excessive amounts of information into a single HD vector. The problem of incremental learning revolves around the ability to regularly update the knowledge within the framework to include new subjects in an online manner. Using an HD computing classifier proved efficient and highly accurate to implement an incremental learning framework as no retraining was required after each online update to the framework. The framework was tested on a total of 11 open source benchmark data sets. A number of experimental tests were preformed to ensure consistent performance of the framework under different conditions against different data sets.

show abstract

Section: Supervisedmentioning

confidence: 99%

An Efficient Hyperdimensional Computing Paradigm for Face Recognition

2022

View full text Add to dashboard Cite

show abstract

“…(3) Compute ( ), � ′ � and ( ) scatter matrices and use it to update ′ using Eqs. (12), (13) and (14). 4Compute � ′ � and use it to update ′ according to Eqs.…”

Section: The Novel Incremental Version Of the Bkndamentioning

confidence: 99%

“…In effect, incremental learning has been deemed to be more adequate than batch learning as new samples are added asynchronously, at different timestamps, or when dealing with a great deal of data. So far, many face recognition methods formulated on incremental learning and dimension reduction have been proposed in literature ( [12] and [13]).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

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.

show abstract

A Review of Face Recognition Using Feature Optimization and Classification Techniques

Raikwar

Agrawal

2020

Algorithms for Intelligent Systems

View full text Add to dashboard Cite

A novel incremental face recognition method based on nonparametric discriminant model

Cited by 5 publications

References 11 publications

An Efficient Hyperdimensional Computing Paradigm for Face Recognition

An Efficient Hyperdimensional Computing Paradigm for Face Recognition

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

A Review of Face Recognition Using Feature Optimization and Classification Techniques

Contact Info

Product

Resources

About