Integration of blcm and flbp in low resolution face recognition

Face recognition from face image has been a fast-growing topic in biometrics research community and a sizeable number of face recognition techniques based on texture analysis have been developed in the past few years. These techniques work well on grayscale and colour images with very few techniques deal with binary and low resolution image. With binary image becoming the preferred format for low face resolution analysis, there is need for further studies to provide a complete solution for image-based face recognition system with higher accuracy. To overcome the limitation of the existing techniques in extracting distinctive features in low resolution images due to the contrast between the face and background, we proposed a statistical feature analysis technique to fill in the gaps. To achieve this, the proposed technique integrates Binary Level Occurrence Matrix (BLCM) and Fuzzy Local Binary Pattern (FLBP) named BLCM-FLBP to extract global and local features of face from face low resolution images. The purpose of BLCM-FLBP 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 face pattern. Experimental results on Yale and FEI datasets validates the superiority of the proposed technique over the other top-performing feature analysis techniques methods by utilizing different classifier which is Neural network (NN) and Random Forest (RF). The proposed technique achieved performance accuracy of 93.16% (RF), 95.27% (NN) when FEI dataset used, and the accuracy of 94.54% (RF), 93.61% (NN) when Yale.B used. Hence, the proposed technique outperforming other technique such as Gray Level Co-Occurrence Matrix (GLCM), Bag of Word (BOW), Fuzzy Local Binary Pattern (FLBP) respectively and Binary Level Occurrence Matrix (BLCM)

Improved relative discriminative criterion using rare and informative terms and ringed seal search-support vector machine techniques for text classification

Classification has become an important task for automatically classifying the documents to their respective categories. For text classification, feature selection techniques are normally used to identify important features and to remove irrelevant, and noisy features for minimizing the dimensionality of feature space. These techniques are expected particularly to improve efficiency, accuracy, and comprehensibility of the classification models in text labeling problems. Most of the feature selection techniques utilize document and term frequencies to rank a term. Existing feature selection techniques (e.g. RDC, NRDC) consider frequently occurring terms and ignore rarely occurring terms count in a class. However, this study proposes the Improved Relative Discriminative Criterion (IRDC) technique which considers rarely occurring terms count. It is argued that rarely occurring terms count are also meaningful and important as frequently occurring terms in a class. The proposed IRDC is compared to the most recent feature selection techniques RDC and NRDC. The results reveal significant improvement by the proposed IRDC technique for feature selection in terms of precision 27%, recall 30%, macro-average 35% and micro- average 30%. Additionally, this study also proposes a hybrid algorithm named: Ringed Seal Search-Support Vector Machine (RSS-SVM) to improve the generalization and learning capability of the SVM. The proposed RSS-SVM optimizes kernel and penalty parameter with the help of RSS algorithm. The proposed RSS-SVM is compared to the most recent techniques GA-SVM and CS-SVM. The results show significant improvement by the proposed RSS-SVM for classification in terms of accuracy 18.8%, recall 15.68%, precision 15.62% and specificity 13.69%. In conclusion, the proposed IRDC has shown better performance as compare to existing techniques because its capability in considering rare and informative terms. Additionally, the proposed RSS- SVM has shown better performance as compare to existing techniques because it has capability to improve balance between exploration and exploitation