Browsing by Author "Mishra, Deepti"

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    PublicationOpen Access
    A Real-Time CNN-Based Lightweight Mobile Masked Face Recognition System
    (IEEE-Inst Electrical Electronics Engineers Inc., 2022) KOCAÇINAR, BÜŞRA; Taş, Bilal; AKBULUT, FATMA PATLAR; Çatal, Çağatay; Mishra, Deepti
    Due to the global spread of the Covid-19 virus and its variants, new needs and problems have emerged during the pandemic that deeply affects our lives. Wearing masks as the most effective measure to prevent the spread and transmission of the virus has brought various security vulnerabilities. Today we are going through times when wearing a mask is part of our lives, thus, it is very important to identify individuals who violate this rule. Besides, this pandemic makes the traditional biometric authentication systems less effective in many cases such as facial security checks, gated community access control, and facial attendance. So far, in the area of masked face recognition, a small number of contributions have been accomplished. It is definitely imperative to enhance the recognition performance of the traditional face recognition methods on masked faces. Existing masked face recognition approaches are mostly performed based on deep learning models that require plenty of samples. Nevertheless, there are not enough image datasets containing a masked face. As such, the main objective of this study is to identify individuals who do not use masks or use them incorrectly and to verify their identity by building a masked face dataset. On this basis, a novel real-time masked detection service and face recognition mobile application was developed based on an ensemble of fine-tuned lightweight deep Convolutional Neural Networks (CNN). The proposed model achieves 90.40% validation accuracy using 12 individuals' 1849 face samples. Experiments on the five datasets built in this research demonstrate that the proposed system notably enhances the performance of masked face recognition compared to the other state-of-the-art approaches.
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    Test Case Prioritization: a Systematic Mapping Study
    (Springer, Van Godewijckstraat 30, 3311 Gz Dordrecht, Netherlands, 2013-09) Çatal, Çağatay; Mishra, Deepti; 108363; 29629
    Test case prioritization techniques, which are used to improve the cost-effectiveness of regression testing, order test cases in such a way that those cases that are expected to outperform others in detecting software faults are run earlier in the testing phase. The objective of this study is to examine what kind of techniques have been widely used in papers on this subject, determine which aspects of test case prioritization have been studied, provide a basis for the improvement of test case prioritization research, and evaluate the current trends of this research area. We searched for papers in the following five electronic databases: IEEE Explorer, ACM Digital Library, Science Direct, Springer, and Wiley. Initially, the search string retrieved 202 studies, but upon further examination of titles and abstracts, 120 papers were identified as related to test case prioritization. There exists a large variety of prioritization techniques in the literature, with coverage-based prioritization techniques (i.e., prioritization in terms of the number of statements, basic blocks, or methods test cases cover) dominating the field. The proportion of papers on model-based techniques is on the rise, yet the growth rate is still slow. The proportion of papers that use datasets from industrial projects is found to be 64 %, while those that utilize public datasets for validation are only 38 %. On the basis of this study, the following recommendations are provided for researchers: (1) Give preference to public datasets rather than proprietary datasets; (2) develop more model-based prioritization methods; (3) conduct more studies on the comparison of prioritization methods; (4) always evaluate the effectiveness of the proposed technique with well-known evaluation metrics and compare the performance with the existing methods; (5) publish surveys and systematic review papers on test case prioritization; and (6) use datasets from industrial projects that represent real industrial problems.