Ensemble of texture and shape descriptors using support vector machine classification for face recognition

VenkateswarLal, Pallavaram; Nitta, Gnaneswara Rao; Prasad, Ande

doi:10.1007/s12652-019-01192-7

Cited by 39 publications

(24 citation statements)

References 21 publications

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“…The experimental result of Liu et al's work shows that the integrated shape and texture features capture the most discriminating information of a face, which contributes to their high recognizing accuracy. Besides their work, other research [58][59][60] demonstrate the advantages of using shape and texture method to represent facial image.…”

Section: Plos Onementioning

confidence: 99%

A face recognition software framework based on principal component analysis

et al. 2021

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Face recognition, as one of the major biometrics identification methods, has been applied in different fields involving economics, military, e-commerce, and security. Its touchless identification process and non-compulsory rule to users are irreplaceable by other approaches, such as iris recognition or fingerprint recognition. Among all face recognition techniques, principal component analysis (PCA), proposed in the earliest stage, still attracts researchers because of its property of reducing data dimensionality without losing important information. Nevertheless, establishing a PCA-based face recognition system is still time-consuming, since there are different problems that need to be considered in practical applications, such as illumination, facial expression, or shooting angle. Furthermore, it still costs a lot of effort for software developers to integrate toolkit implementations in applications. This paper provides a software framework for PCA-based face recognition aimed at assisting software developers to customize their applications efficiently. The framework describes the complete process of PCA-based face recognition, and in each step, multiple variations are offered for different requirements. Some of the variations in the same step can work collaboratively and some steps can be omitted in specific situations; thus, the total number of variations exceeds 150. The implementation of all approaches presented in the framework is provided.

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Section: Plos Onementioning

confidence: 99%

A face recognition software framework based on principal component analysis

et al. 2021

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“…Yet, and choosing only image lines and edges may ignore significant information. Such problem can be solved by choosing efficient ones among produced features overall image, which was a noticeable obstacle [12]. After manipulating the image with set of masks to find the orthogonal polynomial of the image [13], image gradient and Hessian matrix of second derivatives are computed to extract TGH (Peak, Pit, Saddle, Ridge, Ravine, Zero crossing, Flat, Increasing area and Decreasing area) [14], see Figure 1(b).…”

Section: Topographical Features Tghmentioning

confidence: 99%

Face recognition using selected topographical features

Naji

Salman

Fadhil

2020

IJECE

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This paper represents a new features selection method to improve an existed feature type. Topographical (TGH) features provide large set of features by assigning each image pixel to the related feature depending on image gradient and Hessian matrix. Such type of features was handled by a proposed features selection method. A face recognition feature selector (FRFS) method is presented to inspect TGH features. FRFS depends in its main concept on linear discriminant analysis (LDA) technique, which is used in evaluating features efficiency. FRFS studies feature behavior over a dataset of images to determine the level of its performance. At the end, each feature is assigned to its related level of performance with different levels of performance over the whole image. Depending on a chosen threshold, the highest set of features is selected to be classified by SVM classifier

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“…The techniqueses mentioned earlier were not having a clearly defined phase of classification. Several face recognition techniques use various standard classifiers like SVM [22], [7], ANN [12], [14] and [5] in the classification phase of the entire pipeline of the face recognition. These works mainly use some features like Gabor features [12], wavelet [14] and fused them with dimensionality reduction techniques like LDA [12] or PCA [5] and then used classifiers like SVM citea31 or ANN [12], [14], [5].…”

Section: Fig 2: Flow Of Feature Extractionmentioning

confidence: 99%

Smart Gate: Intelligent Security System Based on Face Recognition

Shah*,

Shukla,

Pandya

2020

IJITEE

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Features of Human face is one of the unique biometrics used for identifying and recognizing humans. This makes face recognition system an integral component of numerous applications like identity verification of personnel at gates in many organizations, for controlled access of confidential resources, recognizing intruders by nationwide defense institutions, and such many more. Due to this the unavoidable need of face recognition system, practitioners and researchers are putting their continuous efforts to rectify and optimize the face recognition system for different perspectives in terms of accuracy, time, and storage. The techniques are also optimized for face captured at different degree of orientation, change of facial expression with time, lighting condition, with and without eyeglass and other props, but as isolated solutions. The main aim of the paper is to propose a system that can be installed at any door or gate which would operate based on integrated face recognition. The major contribution of this paper is twofold: i) An integrated face recognition solution optimized for recognizing multiple people faces captured at different orientations and even wearing eyeglasses of different shapes, sizes and materials ii) Smart Gate system being activated upon successful recognition of all faces captured simultaneously.

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Ensemble of texture and shape descriptors using support vector machine classification for face recognition

Cited by 39 publications

References 21 publications

A face recognition software framework based on principal component analysis

A face recognition software framework based on principal component analysis

Face recognition using selected topographical features

Smart Gate: Intelligent Security System Based on Face Recognition

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