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Browsing Scholarly Publication by Author "Abdulrauf Uthman Tosho"

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    A Hybrid Approach for Face Morphing Detection
    (KASU Journal of Computer Science, 2024) Saka Kayode Kamil; Abdulrauf Uthman Tosho; Aro Taye; Seriki Aliu Adebayo; Sulaiman Olaniyi Abdulsalam
    Background: In biometrics, one of the most popular study topics is the detection of face morphing attacks. However, because present methods are unable to capture significant feature changes, they are unable to strike the correct balance between accuracy and complexity. Survey investigation and analysis have shown that the existing method of face morphing detection take a bit longer time to detect the image attack due to the high computation required by facial feature extraction approaches. Conversely, further study is needed to develop a model to enhance the computational time and accuracy of the current face morphing recognition methods. The paper developed a hybrid model for face morphing detection. The FERET database was created to aid in the evaluation and development of algorithms. Local Binary Pattern (LBP) was used as feature extraction algorithm and Residue Number System (RNS) was introduced to reduce the lengthy computational time of LBP during the extraction of images. The classification accuracy of 98% was achieved for the FERET database, while an accuracy of 96% was achieved for the FRGCv2 database. An average training time of 0.0532seconds was recorded for the FERET database, while an average training time of 0.0582seconds was achieved for the FRGCv2 database. The study concluded that the high dimensionality of LBP was well reduced and optimized by the RNS algorithm, which improved the performance of face morphing recognition
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    FACE MORPHING DETECTION USING LOCAL BINARY PATTERN AND CHINESE REMINDER THEOREM
    (Sule Lamido University Journal of Science & Technology, 2024) Kadir Akeem Femi; Abdulrauf Uthman Tosho; Saka Kayode Kami; Aro Taiye
    Face biometrics offer a wide range of uses since they are straightforward for human observers to verify during passport issuing and border control. However, the development of facial biometric attacks has made it possible to evade passport issuance procedures, as demonstrated by morphing attacks. Due to the lengthy computation times associated with facial feature extraction techniques, survey research has revealed that the current face morphing detection method took a while to identify the image attack. Conversely, further study is needed to develop a model to enhance the current face morphing recognition methods. The aim of this study is to develop an enhanced face morphing recognition system using the Local Binary Pattern (LBP) and Residue Number System (RNS). We combine elements from CRT and LBP to extract features from both bona fide and morph images. Extensive experiments are carried out on FRGCv2 datasets to benchmark the proposed method's detection performance and the existing methods. The best classification accuracy of 98% was achieved for the FRGCv2. An equal error rate of 0.02370 was achieved for the FRGCv2 database. The study concluded that the high dimensionality of LBP was well reduced and optimized by the CRT algorithm, which improved the performance of face morphing recognition. The obtained results indicate that the proposed LBP-CRT outperforms existing system with respect to training time, equal error rate, and accuracy of morph detection