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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI)

Shahreen Kasim

Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia

Author-ID : 2606340

Computer Science & IT Electrical & Electronics Engineering

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Articles

Fusion Iris and Periocular Recognitions in Non-Cooperative Environment Anis Farihan Mat Raffei; Tole Sutikno; Hishammuddin Asmuni; Rohayanti Hassan; Razib M Othman; Shahreen Kasim; Munawar A Riyadi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 7, No 3: September 2019
Publisher : IAES Indonesian Section

The performance of iris recognition in non-cooperative environment can be negatively impacted when the resolution of the iris images is low which results in failure to determine the eye center, limbic and pupillary boundary of the iris segmentation. Hence, a combination with periocular features is suggested to increase the authenticity of the recognition system. However, the texture feature of periocular can be easily affected by a background complication while the colour feature of periocular is still limited to spatial information and quantization effects. This happens due to different distances between the sensor and the subject during the iris acquisition stage as well as image size and orientation. The proposed method of periocular feature extraction consists of a combination of rotation invariant uniform local binary pattern to select the texture features and a method of color moment to select the color features. Besides, a hue-saturation-value channel is selected to avoid loss of discriminative information in the eye image. The proposed method which consists of combination between texture and colour features provides the highest accuracy for the periocular recognition with more than 71.5% for the UBIRIS.v2 dataset and 85.7% for the UBIPr dataset. For the fusion recognitions, the proposed method achieved the highest accuracy with more than 85.9% for the UBIRIS.v2 dataset and 89.7% for the UBIPr dataset.

Selecting Root Exploit Features Using Flying Animal-Inspired Decision Ahmad Firdaus; Mohd Faizal Ab Razak; Wan Isni Sofiah Wan Din; Danakorn Nincarean; Shahreen Kasim; Tole Sutikno; Rahmat Budiarto
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 7, No 4: December 2019
Publisher : IAES Indonesian Section

Malware is an application that executes malicious activities to a computer system, including mobile devices. Root exploit brings more damages among all types of malware because it is able to run in stealthy mode. It compromises the nucleus of the operating system known as kernel to bypass the Android security mechanisms. Once it attacks and resides in the kernel, it is able to install other possible types of malware to the Android devices. In order to detect root exploit, it is important to investigate its features to assist machine learning to predict it accurately. This study proposes flying animal-inspired (1) bat, 2) firefly, and 3) bee) methods to search automatically the exclusive features, then utilizes these flying animal-inspired decision features to improve the machine learning prediction. Furthermore, a boosting method (Adaboost) boosts the multilayer perceptron (MLP) potential to a stronger classification. The evaluation jotted the best result is from bee search, which recorded 91.48 percent in accuracy, 82.2 percent in true positive rate, and 0.1 percent false positive rate.

Co-Authors Ahmad Firdaus Anis Farihan Mat Raffei Danakorn Nincarean Hishammuddin Asmuni Mohd Faizal Ab Razak Munawar A Riyadi Rahmat Budiarto Razib M Othman Rohayanti Hassan Tole Sutikno Wan Isni Sofiah Wan Din

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