Yazar "Eleyan, Alaa" seçeneğine göre listele

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    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.
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    Facial expression recognition based on image pyramid and single-branch decision tree
    (SPRINGER LONDON LTD, 2017) Ashir, Abubakar M.; Eleyan, Alaa
    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 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.
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    Facial expression recognition with dynamic cascaded classifier
    (Springer London, 2019) Ashir, Abubakar Muhammad; Eleyan, Alaa; Akdemir, Bayram
    In this paper, a new approach for facial expression recognition has been proposed. The approach has imbedded a new feature extraction technique, new multiclass classification approach and a new kernel parameter optimization for support vector machines. The scheme of the approach begins with feature extraction from the input vectors, and the extracted features are transformed into a Gaussian space using compressive sensing techniques. This process ensures feature vector dimensionality reduction and matches the features vectors with radial basis function kernel used in support vector machines for classification. Prior to classification, an optimized parameter for support vector machines training is automatically determined based on an approach proposed which relies on the receiver operating characteristics of the support vector machine classifier. With the optimized kernel parameter, new proposed multiclass classification approach is used to finally classify any vector. In all 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. The results further confirmed the validity and advantages of the proposed approach over other approaches currently used in the literature. © 2019, Springer-Verlag London Ltd., part of Springer Nature.
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    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.
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    Performance Evaluation for Face Recognition Using Wavelet-based Image De-noising
    (IEEE, 2013) Atamuradov, Vepa; Eleyan, Alaa; Karlik, Bekir
    In this research we scrutinize the face recognition system performance when the test images are imposed to different levels of noise. We tried to imitate the real world scenarios when the face images are captured from video cameras or scanners and suffer some noise. To investigate the performance of proposed system, we simulate this scenario by adding A WGN (additive white Gaussian noise) to the test images in the face database. For image de-noising, we used two different algorithms namely; Discrete Wavelets Transform (DWT) and Dual-Tree Complex Wavelets Transform (DTCWT). The de-noised images are then fed to a PCA-based face recognition system for better recognition performance.
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    Sift-based Iris Recognition Using Sub-Segments
    (IEEE, 2013) Mesecan, Ibrahim; Eleyan, Alaa; Karlik, Bekir
    In this paper, we investigate the use of Scale Invariant Feature Transform (SIFT) for iris recognition problem with sub-segments. Instead of using the whole iris, we extracted sub-segments from the iris image for classification. These sub-segments were used separately for classification. Also, feature based fusion is applied using different sub-segments from the same iris. A preprocessing step for cropping the iris area from the images was address in this paper as well to increase performance of the system. The simulation results show high performance on the used database.