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 image-based face recognition system with a higher accuracy rate. To overcome the limitation of the existing methods in extracting distinctive features in low-resolution images due to the contrast between the face and background, we propose a statistical feature analysis technique to fill the gaps. To achieve this, the proposed technique integrates the binary-level occurrence matrix (BLCM) and the fuzzy local binary pattern (FLBP) named FBLCM to extract global and local features of the face from binary and low-resolution images. The purpose of FBLCM is to distinctively improve performance of edge sharpness between black and white pixels in the binary image and to extract significant data relating to the features of the face pattern. Experimental results on Yale and FEI datasets validate the superiority of the proposed technique over the other top-performing feature analysis methods. The developed technique has achieved the accuracy of 94.54% when a random forest classifier is used, hence outperforming other techniques such as the gray-level co-occurrence matrix (GLCM), bag of word (BOW), and fuzzy local binary pattern (FLBP), respectively.

CFNN for Identifying Poisonous Plants

“…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.…”

Features Extraction for Robust Face Recognition Using GLCM and CS-LBP

“…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.…”

Improved local descriptor (ILD): a novel fusion method in face recognition

“…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.…”

“…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...…”