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)

Development of a robust multi-scale featured local binary pattern for improved facial expression recognition

Compelling facial expression recognition (FER) processes have been utilized in very successful fields like computer vision, robotics, artificial intelligence, and dynamic texture recognition. However, the FER’s critical problem with traditional local binary pattern (LBP) is the loss of neighboring pixels related to different scales that can affect the texture of facial images. To overcome such limitations, this study describes a new extended LBP method to extract feature vectors from images, detecting each image from facial expressions. The proposed method is based on the bitwise AND operation of two rotational kernels applied on LBP(8,1)and LBP(8,2)and utilizes two accessible datasets. Firstly, the facial parts are detected and the essential components of a face are observed, such as eyes, nose, and lips. The portion of the face is then cropped to reduce the dimensions and an unsharp masking kernel is applied to sharpen the image. The filtered images then go through the feature extraction method and wait for the classification process. Four machine learning classifiers were used to verify the proposed method. This study shows that the proposed multi-scale featured local binary pattern (MSFLBP), together with Support Vector Machine (SVM), outperformed the recent LBP-based state-of-the-art approaches resulting in an accuracy of 99.12% for the Extended Cohn–Kanade(CK+) dataset and 89.08% for the Karolinska Directed Emotional Faces(KDEF)dataset