Study on the performance of moments as invariant descriptors for practical face recognition systems

Farajzadeh, Nacer; Faez, Karim; Pan, Gang

doi:10.1049/iet-cvi.2009.0140

Cited by 24 publications

(7 citation statements)

References 26 publications

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“…To determine image descriptors that are able to improve classification performance of multioption recognition as well as pair matching of face images seems to be a complex problem [65,69,70].…”

Section: Face Descriptor-based Methodsmentioning

confidence: 99%

Face Recognition: Issues, Methods and Alternative Applications

Wójcik¹,

Gromaszek²,

Junisbekov³

2016

Face Recognition - Semisupervised Classification, Subspace Projection and Evaluation Methods

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Face recognition, as one of the most successful applications of image analysis, has recently gained significant attention. It is due to availability of feasible technologies, including mobile solutions. Research in automatic face recognition has been conducted since the s, but the problem is still largely unsolved. Last decade has provided significant progress in this area owing to advances in face modelling and analysis techniques. Although systems have been developed for face detection and tracking, reliable face recognition still offers a great challenge to computer vision and pattern recognition researchers. There are several reasons for recent increased interest in face recognition, including rising public concern for security, the need for identity verification in the digital world, face analysis and modelling techniques in multimedia data management and computer entertainment. In this chapter, we have discussed face recognition processing, including major components such as face detection, tracking, alignment and feature extraction, and it points out the technical challenges of building a face recognition system. We focus on the importance of the most successful solutions available so far. The final part of the chapter describes chosen face recognition methods and applications and their potential use in areas not related to face recognition.

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Section: Face Descriptor-based Methodsmentioning

confidence: 99%

Face Recognition: Issues, Methods and Alternative Applications

Wójcik¹,

Gromaszek²,

Junisbekov³

2016

Face Recognition - Semisupervised Classification, Subspace Projection and Evaluation Methods

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“…Developing image descriptors that are able to improve classification performance of multi‐option recognition as well as pair matching of face images is investigated in the literature [144, 146, 147]. The main idea behind developing image descriptors of these works is to learn the most discriminant local features that can minimise the difference of the features between images of a same individual and maximise that between images from other people depending on the nature of these descriptors, which compute an image representation from local patch statistics.…”

Section: Literature Reviewmentioning

confidence: 99%

Face recognition: challenges, achievements and future directions

Hassaballah

Aly

2015

IET Computer Vision

156

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Face recognition has received significant attention because of its numerous applications in access control, law enforcement, security, surveillance, Internet communication and computer entertainment. Although significant progress has been made, the state-of-the-art face recognition systems yield satisfactory performance only under controlled scenarios and they degrade significantly when confronted with real-world scenarios. The real-world scenarios have unconstrained conditions such as illumination and pose variations, occlusion and expressions. Thus, there remain plenty of challenges and opportunities ahead. Latterly, some researchers have begun to examine face recognition under unconstrained conditions. Instead of providing a detailed experimental evaluation, which has been already presented in the referenced works, this study serves more as a guide for readers. Thus, the goal of this study is to discuss the significant challenges involved in the adaptation of existing face recognition algorithms to build successful systems that can be employed in the real world. Then, it discusses what has been achieved so far, focusing specifically on the most successful algorithms, and overviews the successes and failures of these algorithms to the subject. It also proposes several possible future directions for face recognition. Thus, it will be a good starting point for research projects on face recognition as useful techniques can be isolated and past errors can be avoided.

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“…Families of DOMs such as Racah [3], dual Hahn [4], and Tchebichef [5] have been used successfully in many applications, for instance, feature analysis [6,7], face recognition [8,9], and image retrieval [10,11] to name a few. Particularly, medical imaging has exploited orthogonal moments to characterize different types of biological tissue and assess the severity of several diseases [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study on Discrete Shmaliy Moments and Their Texture-Based Applications

González

Nava

Escalante-Ramı́rez

2018

Mathematical Problems in Engineering

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In recent years, discrete orthogonal moments have attracted the attention of the scientific community because they are a suitable tool for feature extraction. However, the numerical instability that arises because of the computation of high-order moments is the main drawback that limits their wider application. In this article, we propose an image classification method that avoids numerical errors based on discrete Shmaliy moments, which are a new family of moments derived from Shmaliy polynomials. Shmaliy polynomials have two important characteristics: one-parameter definition that implies a simpler definition than popular polynomial bases such as Krawtchouk, Hahn, and Racah; a linear weight function that eases the computation of the polynomial coefficients. We use IICBU-2008 database to validate our proposal and include Tchebichef and Krawtchouk moments for comparison purposes. The experiments are carried out through 5-fold cross-validation, and the results are computed using random forest, support vector machines, naïve Bayes, and k-nearest neighbors classifiers.

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Study on the performance of moments as invariant descriptors for practical face recognition systems

Cited by 24 publications

References 26 publications

Face Recognition: Issues, Methods and Alternative Applications

Face Recognition: Issues, Methods and Alternative Applications

Face recognition: challenges, achievements and future directions

A Comparative Study on Discrete Shmaliy Moments and Their Texture-Based Applications

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