Omid Sharifi - Academia.edu (original) (raw)

Papers by Omid Sharifi

Çukurova Üniversitesi Mühendislik Mimarlık Fakültesi dergisi, Dec 31, 2020

Malicious parties which impersonate systems by fake identities affect recognition performance of ... more Malicious parties which impersonate systems by fake identities affect recognition performance of biometric systems. This study focuses on a strength anti-spoofing scheme based on decision level fusion to monitor individuals in term of real and fake. The proposed fake detection scheme involves consideration of both handcrafted and deep learned techniques on face images to differentiate real and fake individuals. In this context, convolutional neural network (CNN) and Log-Gabor filter methods are used to learn deep representations and extract facial features of images respectively. In order to improve the robustness of proposed anti-spoofing framework, fusion of Log-Gabor and CNN methods is considered by applying decision-level-fusion technique. Finally, the performance of proposed antispoofing scheme is examined on public spoof databases such as Print-Attack and Replay-Attack face databases to detect fake facial images.

IET Biometrics, Jan 13, 2017

Designing a new dynamic and optimal scheme for face-iris fusion based on the score level, feature... more Designing a new dynamic and optimal scheme for face-iris fusion based on the score level, feature level and decision level fusion is considered in this study. Prior to implementing the proposed combined level fusion, several schemes are separately implemented at each level of fusion to investigate the performance improvement of each level of fusion on face and iris modalities. In fact, the optimum scheme is constructed by selecting flexible and dynamic features and scores of face and iris biometrics and then combining the advantages of different levels of fusion. Consequently, the scheme produces a set of fast and flexible features and scores for fusion. On the other hand, the idea of threshold-optimised decisions is used in this study to fuse the optimised decisions of face and iris biometrics. Experimental results on verification rates demonstrate a significant improvement of proposed combined level fusion scheme over unimodal and multimodal fusion methods.

Communications in computer and information science, 2016

Web Service Modeling Language (WSML), based on the Web Service Modeling Ontology (WSMO), is a lar... more Web Service Modeling Language (WSML), based on the Web Service Modeling Ontology (WSMO), is a large and highly complex language designed for the specification of semantic web services. It has different variants based on logical formalisms, such as Description Logics, First-Order Logic and Logic Programming. We perform an in-depth study of both WSMO and WSML, critically evaluating them by identifying their strong points and areas in which improvement would be beneficial. Our studies show that in spite of all the features WSMO and WSML support, their sheer size and complexity are major weaknesses, and there are other areas in which important deficiencies exist as well. We point out those discovered deficiencies, and propose remedies for them, laying the foundation for a more tractable and useful formalism for specifying semantic web services.

IET Biometrics, Feb 5, 2019

This study is concerned with analysing face-ocular multimodal biometric systems for a person gend... more This study is concerned with analysing face-ocular multimodal biometric systems for a person gender prediction. Particularly, this is the first study considering fusion of face and ocular biometrics to predict gender of a person via a hybrid multimodal scheme. The authors aim to investigate the effect of multimodal biometric systems at score and feature-level fusion on gender classification. The implementation of uniform local binary pattern (ULBP) feature extractor is taken into account to extract the face and ocular texture information. This paper proposes to select the efficient feature sets of both modalities using a novel evolutionary algorithm called backtracking search algorithm (BSA). On the other hand, support vector machine (SVM) is applied for classification purpose using the fused face and ocular features and scores. The proposed scheme is validated using CASIA-Iris-Distance and MBGC multimodal biometric databases with consideration of a subject-disjoint training and testing evaluation. The achieved gender recognition demonstrates the superiority of the hybrid multimodal face-ocular scheme over unimodal face and ocular schemes implemented in this study for a subject gender prediction.

Multimedia Tools and Applications, Apr 22, 2023

Expert Systems, Feb 28, 2019

Matching web services and client requirements in the form of goals is a significant challenge in ... more Matching web services and client requirements in the form of goals is a significant challenge in the discovery of semantic web services. The most common but unsatisfactory approach to matching is set-based, where both the client and web services declare what objects they require and what objects they can provide. Matching then becomes the simple task of comparing sets of objects. This approach is inadequate because it says nothing about the functionality required by the client or the functionality provided by the web service. As an alternative, we use the Frame Logic as implemented in Flora-2 to specify web service capabilities and client requirements, including their preconditions, postconditions, and ontologies, implement a logic-based discovery agent using Flora-2, demonstrate its usefulness in a medical appointment making scenario, and show its efficiency both theoretically and by benchmarking. The result is an expressive yet concise representation scheme for semantic web services, and a practical, efficient, powerful, and fully implemented matching engine based purely on logical inference for web service discovery, with direct applicability to Web Service Modeling Ontology and Web Service Modeling Language, because both are based on Frame Logic.

International Journal of Pattern Recognition and Artificial Intelligence, Feb 26, 2022

This study presents a novel facial makeup detection scheme using fusion of multi-scale Local Bina... more This study presents a novel facial makeup detection scheme using fusion of multi-scale Local Binary Patterns (ms-LBP) texture methods and convolutional neural network (CNN) deep learning extractor. Facial makeups affect the accuracy of face recognition systems due to appearance alteration of individuals. In order to fuse the facial features, the proposed scheme first considers concatenation of extracted global histogram of a whole image using three different LBP operators. The LBP scales are used to extract and then concatenate the local histogram of overlapping and nonoverlapping blocks of images. This way utilizes the advantages of microtexton information of local primitives besides the extracted global textures. Finally, the extracted features using CNN feature extractor are combined with global and local multi-scale textures. In general, CNN deep learning extractor learns high-level discriminative characteristics of images and therefore the proposed system improves the facial makeup detection rate by involving both microtexton and discriminative information of facial images. In addition, the proposed method attempts to select the optimized subset of facial features to increase the detection performance of system by applying Particle Swarm Optimization (PSO) technique after feature level fusion. The paper uses Support Vector Machine (SVM) classifier for classifying the facial vectors into makeup or no-makeup classes. The proposed scheme is then evaluated using YMU, VMU and MIW facial makeup databases with consideration of light, medium and heavy makeups on several datasets. Experimental results analysis clarifies the effectiveness of proposed facial makeup detection framework of this study.

Journal of Logic and Computation, Sep 10, 2014

ABSTRACT The original unification algorithm for F-Logic molecules presented by Kifer et al. 1995,... more ABSTRACT The original unification algorithm for F-Logic molecules presented by Kifer et al. 1995, contains a mistake. We identify the mistake and demonstrate it through two examples, suggest a solution to the problem in the original algorithm and prove that the resulting algorithm correctly unifies F-Logic molecules.

International Journal of Image, Graphics and Signal Processing, Feb 8, 2019

Recognition performance of biometric systems is affected through spoofing attacks made by fake id... more Recognition performance of biometric systems is affected through spoofing attacks made by fake identities. The focus of this paper is on presenting a new scheme based on score level and decision level fusion to monitor individuals in term of real and fake. The proposed fake detection scheme involve consideration of both handcrafted and deep learned techniques on face images to differentiate real and fake individuals. In this approach, convolutional neural network (CNN) and overlapped histograms of local binary patterns (OVLBP) methods is used to extract facial features of images. The produced matching scores provided by CNN and OVLBP then combined to form a fused score vector. Finally, the last decision on real and attack images is done by combining decisions of hybrid scheme using majority vote of CNN, OVLBP and their fused vector. Experimental results on public spoof databases such as Print-Attack and Replay-Attack face databases demonstrate the strength of the proposed anti-spoofing method for fake detection.

International Journal of Image, Graphics and Signal Processing, 2019

Artificial Intelligence Review, 2011

Evolutionary multi objective optimization (EMOO) systems are evolutionary systems which are used ... more Evolutionary multi objective optimization (EMOO) systems are evolutionary systems which are used for optimizing various measures of the evolving system. Rule mining has gained attention in the knowledge discovery literature. The problem of discovering rules with specific properties is treated as a multi objective optimization problem. The objectives to be optimized being the metrics like accuracy, comprehensibility, surprisingness, novelty to

Multimedia Tools and Applications

Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 2020

2017 International Conference on Computer Science and Engineering (UBMK)

Face recognition is considered as one of the relatively new and interesting concepts in the area ... more Face recognition is considered as one of the relatively new and interesting concepts in the area of biometrics and comprises a huge number of applications. This study involves implementation of a robust recognition system by employing global and random local facial features of an individual. The proposed scheme considers the extraction of global facial features and some randomly selected local facial features. In order to exploit global information of a whole face image, Principal Component Analysis (PCA) algorithm is used. On the other hand, the randomly selected sub-images of a whole face image are concatenated and subsequently PCA is applied on the concatenated regions. In addition, the proposed scheme involves the optimization of random sub-image locations and sizes by considering different settings and choosing the most optimized one. Finally, Weighted Sum Rule fusion is employed to combine the calculated scores of the global and local feature extractors. The reliability of the proposed facial recognition system is investigated on several data sets of ORL, FERET, and Extended Yale face databases. Demonstration of results based on the recognition performance and ROC analysis clarifies that the proposed scheme achieves a considerable improvement compared to the global and local feature extractors implemented in this study.

International Journal of Pattern Recognition and Artificial Intelligence, 2022

Communications in Computer and Information Science, 2016

Lecture Notes in Computer Science, 2018

Spoofing attacks provided by fake individuals are considered as a major interest on biometric sys... more Spoofing attacks provided by fake individuals are considered as a major interest on biometric systems. To implement a robust face biometric system, deploying a reliable anti-spoofing scheme is needed. The concentration of this study to organize the anti-spoofing technique is on overlapped face textures together with image quality assessment. Our proposed fake detection scheme applies double anti-spoofing solution to distinguish live and fake identities. Firstly, image quality assessment method is used to differentiate fake and real samples by comparing their quality. The fake detection method using overlapped histograms of LBP texture descriptor is then applied for those samples recognized as real to increase the robustness of the biometric system against unreliable quality of images. Proposed spoof detection method presents an effective strategy for detecting fake face samples for video and print attacks. Demonstration of results on public spoof databases clarifies the robustness of the proposed solution for face fake detection.

Doctor of Philosophy in Computer Engineering. Thesis (M.S.)--Eastern Mediterranean University, Fa... more Doctor of Philosophy in Computer Engineering. Thesis (M.S.)--Eastern Mediterranean University, Faculty of Engineering, Dept. of Computer Engineering, 2014. Supervisor: Assoc. Prof. Dr. Zeki Bayram.

Journal of Logic and Computation, 2014

Symmetry

Cosmetics pose challenges to the recognition performance of face and iris biometric systems due t... more Cosmetics pose challenges to the recognition performance of face and iris biometric systems due to its ability to alter natural facial and iris patterns. Facial makeup and iris contact lens are considered to be commonly applied cosmetics for the face and iris in this study. The present work aims to present a novel solution for the detection of cosmetics in both face and iris biometrics by the fusion of texture, shape and color descriptors of images. The proposed cosmetic detection scheme combines the microtexton information from the local primitives of texture descriptors with the color spaces achieved from overlapped blocks in order to achieve better detection of spots, flat areas, edges, edge ends, curves, appearance and colors. The proposed cosmetic detection scheme was applied to the YMU YouTube makeup database (YMD) facial makeup database and IIIT-Delhi Contact Lens iris database. The results demonstrate that the proposed cosmetic detection scheme is significantly improved compared to the other schemes implemented in this study.