2020
DOI: 10.1155/2020/4967034
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A Novel Statistical Feature Analysis-Based Global and Local Method for Face Recognition

Abstract: Face recognition from an image/video has been a fast-growing area in research community, and a sizeable number of face recognition techniques based on texture analysis have been developed in the past few years. Further, these techniques work well on gray-scale and colored images, but very few techniques deal with binary and low-resolution images. As the binary image is becoming the preferred format for low face resolution analysis, there is a need for further studies to provide a complete solution for the imag… Show more

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Cited by 17 publications
(5 citation statements)
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“…Features extraction phase aims to extract robust and relevant characteristics from image, features can be classified into: statistical features, shape features, color features and texture features 8 . In this phase combination of shape and statistical features are extracted, (11) relevant and robust features are extracted from leaf image: (4) were the first order statistics features and ( 7) features were extracted from the shape of leaf image.…”
Section: Features Extractionmentioning
confidence: 99%
“…Features extraction phase aims to extract robust and relevant characteristics from image, features can be classified into: statistical features, shape features, color features and texture features 8 . In this phase combination of shape and statistical features are extracted, (11) relevant and robust features are extracted from leaf image: (4) were the first order statistics features and ( 7) features were extracted from the shape of leaf image.…”
Section: Features Extractionmentioning
confidence: 99%
“…The main visual feature of an image is the texture that is considered the most powerful facial descriptors due to the robustness of extracting this feature to alterations of facial expression, pose, etc. [16][17][18]. Figure 1 shows feature face.…”
Section: Face Recognition Systemmentioning
confidence: 99%
“…The testing must be performed on large scale benchmarks to explore the descriptors more. Talab et al [ 12 ] develop the Fuzzy Binary Level Co-occurrence Matrix (FBLCM) method by joining BLCM and Fuzzy LBP (FLBP). FBLCM motive is to improve explicitly the performance in edges among white and black pixels of binary image.…”
Section: Introductionmentioning
confidence: 99%
“…Better in light & rotation change The novelty is not enough to declare them as efficient descriptor and no global method is used LBP + HOG + AC-LBP [ 9 ] The concept of arithmetic coding and merger with LBP and HOG proves very impressive Testing has been performed on small benchmarks and no machine learning algorithm is used MB-LBPUH [ 10 ] Feature dimension is on lower side and integration with HOG yields much stronger descriptor The demerit in this work is that PCA is used for feature compression GBSBP + LPQ [ 11 ] Effective in various image transformations such as light, emotion, scale and pose. Novel methodology is introduced in GBSBP Evaluation is performed on small datasets FBLCM [ 12 ] FBLCM explicitly improve the performance in edges among white and black pixels of binary image The evaluation is conducted on small datasets Gaussian + Gabor + LBP [ 13 ] The method is invariant to light and noise changes Lack of global feature extraction and testing only on single dataset LOG + HOG [ 14 ] This method produces effective results on plastic surgery dataset Results are restricted only to single dataset DCT + PCA [ 15 ] Reduces the feature size and extracts the best features for classification by using bat algo Only two datasets are used for the evaluation Machine Learning [ 16 ] Various feature extraction techniques and machine learning approaches are evaluated The deep neural networks are not used for accuracy enhancement Geometric + Histogram [ 17 ] Th integration proves out as the effective met...…”
Section: Introductionmentioning
confidence: 99%