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Öğe Facial expression recognition based on compressive sensing and pyramid processing(PAMUKKALE UNIV, 2017) Eleyan, Alaa; Ashir, Abubakar M.In this paper, a new approach has been proposed for improved facial expression recognition. The new approach is inspired by the compressive sensing theory and multi -resolution approach to facial expression problems. Initially, each image sample is decomposed into desired levels of its pyramids at different sizes and resolutions. At each level of the pyramid, features are extracted using a measurement matrix based on compressive sensing theory. These measurements are concatenated together to form a feature vector for the original image. The results obtained from the approach using three distance measurement classifiers (Manhattan, Euclidean, Cosine) and support vector machine are impressive and outperforms most of its counterpart algorithms in the literature using the same databases and settings.Öğe Facial expression recognition based on image pyramid and single-branch decision tree(SPRINGER LONDON LTD, 2017) Ashir, Abubakar M.; Eleyan, AlaaIn this paper, a new approach has been proposed for improved facial expression recognition. The new approach is inspired by the compressive sensing theory and multiresolution approach to facial expression problems. Initially, each image sample is decomposed into desired pyramid levels at different sizes and resolutions. Pyramid features at all levels are concatenated to form a pyramid feature vector. The vectors are further reinforced and reduced in dimension using a measurement matrix based on compressive sensing theory. For classification, a multilevel classification approach based on single-branch decision tree has been proposed. The proposed multilevel classification approach trains a number of binary support vector machines equal to the number of classes in the datasets. Class of test data is evaluated through the nodes of the tree from the root to its apex. The results obtained from the approach are impressive and outperform most of its counterparts in the literature under the same databases and settings.Öğe Facial Expression Recognition with an Optimized Radial Basis Kernel(IEEE, 2018) Ashir, Abubakar M.; Akdemir, BayramIn this work, a new approach for facial expression recognition has been proposed. The approach has imbedded in it both new feature extraction technique and classification techniques using automatic auto-tuning of kernel parameter optimization in support vector machines. It generally begins with feature extraction from the input vectors using a combination of arithmetic means difference and rotation invariant Local Binary Pattern. The extracted features are projected into a Gaussian space to match it with the radial basis function kernel used in support vector machines for classification. Prior to classification, an optimized parameter for support vector machines training are automatically determined based on an approach proposed which relies on the receiver operating characteristics of the support vector machine classifier. The results obtained from the experiments were impressive and promising. From the experiments conducted on the two facial expression databases with different cross-validation techniques, the proposed approach outperforms its counterparts under the same database and settings.Öğe A monogenic local gabor binary pattern for facial expression recognition(Selçuk Üniversitesi Mühendislik Fakültesi, 2017) Eleyan, Alaa; Ashir, Abubakar M.The paper implements a monogenic-Local Binary Pattern (mono-LBP) algorithm on Local Gabor Pattern (LGP). The proposed approach initially features from the samples using LGP at different scales and orientation. The extracted LGP features are further enhanced by decomposing it into three monogenic LBP channels before being recombined to generate the final feature vector. Different Normalization schemes are applied to the final feature vector. Two best performing normalization algorithms with mono-LBP are fused at score level to obtain an improved performance using K-Nearest Neighbor classifier with L1-norm as a distance metrics. Moreover, performance comparison is done with other variants of LGP algorithm and also the effects of various normalization techniques are investigated. Experimental results from JAFFE and TFEID facial expression databases show that the new technique has improved performance compared to its counterparts.