A Review on Feature Extraction Techniques in Face Recognition

Rouhi, Rahimeh

doi:10.5121/sipij.2012.3601

Cited by 18 publications

(15 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Image feature can be obtained after the color space of the image is specified. Features resulted in a variety of research on the color feature are histograms, color moments (CM), color coherence vector and the color correlogram [5,8,9,10,11,12]. There is dominant color descriptor (DCD), color structure descriptor (CSD), and Scalable color descriptor respectively for color feature extraction [13].…”

Section: Feature Extraction Of Color Texture and Shapementioning

confidence: 99%

“…In general, a method for extracting texture feature can be divided into two categories based on spatial domain (spatial texture) and frequency domain (spectral texture). Spatial texture feature is obtained by calculating the statistical values of the pixel or finding a local pixel structure in the original image [10]. Spectral texture feature is obtained by making a transformation into the frequency domain and calculating features from the image transformed.…”

Section: -3mentioning

confidence: 99%

“…Contour-based method gains shape feature by calculating boundary shape of the image, while region-based method gains shape feature from the entire region of the image [9]. Extraction methods used in various batik image processing include the classification of Batik, Batik pattern recognition, Batik image retrieval, and batik image search system [10,15]. Shape representation classification and description techniques are shown in Fig.…”

Section: -3mentioning

confidence: 99%

See 2 more Smart Citations

Feature extraction methods for batik pattern recognition: A review

Kasim¹,

Wardoyo²,

Harjoko³

2016

AIP Conference Proceedings

View full text Add to dashboard Cite

Section: Feature Extraction Of Color Texture and Shapementioning

confidence: 99%

Section: -3mentioning

confidence: 99%

Section: -3mentioning

confidence: 99%

See 1 more Smart Citation

Feature extraction methods for batik pattern recognition: A review

Kasim¹,

Wardoyo²,

Harjoko³

2016

AIP Conference Proceedings

View full text Add to dashboard Cite

“…Face recognition was deeply studied through the last decades [1]. Several types of features were presented with different levels of recognition accuracy [2]. Each type of feature represent an amount of information extracted from the image, and more represented information in chosen feature provides more accuracy in recognition results [3].…”

Section: Introductionmentioning

confidence: 99%

Face recognition using selected topographical features

Naji

Salman

Fadhil

2020

IJECE

View full text Add to dashboard Cite

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

show abstract

“…Besides that, Rouhi et al [8] have tested and analysed several other feature extraction methods namely Gabor Filter, and Elastic Bunch Graph Matching (EBGM). Rouhi et al [8] found that 15-Gabor filter with fuzzy filter leads to a high rate of the feature extraction in the face recognition.…”

Section: Introductionmentioning

confidence: 99%

A Study on Facial Expression Recognition Using Local Binary Pattern

Kasim¹,

Hassan²,

Zaini³

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

View full text Add to dashboard Cite

How to get the proper combination of feature extraction and classification is still crucial in facial expression recognition, and it has been addressed conducted over two decades. Hence, if inadequate features are used, even the best classifier could fail to achieve the accurate recognition. Therefore, Local Binary Pattern (LBP) is used as a feature extraction technique for facial expressions recognition where it is evaluated based on statistical local features. LBP is proven successful technique by the recent study due to its speed and discrimination performance aside of robust to low-resolution images. For the classification, Support Vector Machine is chosen, and the algorithm is implemented in MATLAB and tested on JAFFE (Japanese Female Facial Expressions) database in order to achieve the objectives and the goal of this research which is to obtain high accuracy in facial expressions and identify the seven basic facial expressions. The performance of feature extraction and classification is evaluated based on the recognition accuracy. The observation on results obtained in facial expressions recognition rate indicated the effectiveness of the proposed algorithm based on SVM-LBP features.

show abstract

A Review on Feature Extraction Techniques in Face Recognition

Abstract: ABSTRACT

Cited by 18 publications

References 18 publications

Feature extraction methods for batik pattern recognition: A review

Feature extraction methods for batik pattern recognition: A review

Face recognition using selected topographical features

A Study on Facial Expression Recognition Using Local Binary Pattern

Contact Info

Product

Resources

About