Spoofing detection on facial images recognition using LBP and GLCM combination

Sthevanie, Febryanti; Ramadhani, Kurniawan Nur

doi:10.1088/1742-6596/971/1/012014

Cited by 19 publications

(5 citation statements)

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“…Four different test scenarios were used in the study. The best results were obtained by applying LBP and GLCM matrices to the eye and nose regions [ 35 ]. Khurshid et al developed a system that detects real-time FPA over textural features.…”

Section: Related Workmentioning

confidence: 99%

Face presentation attack detection performances of facial regions with multi-block LBP features

Yılmaz

Turhal

Nabiyev

2023

Multimed Tools Appl

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Biometric recognition systems are frequently used in daily life although they are vulnerable to attacks. Today, especially the increasing use of face authentication systems has made these systems the target of face presentation attacks (FPA). This has increased the need for sensitive systems detecting the FPAs. Recently surgical masks, frequently used due to the pandemic, directly affect the performance of face recognition systems. Researchers design face recognition systems only from the eye region. This motivated us to evaluate the FPA detection performance of the eye region. Based on this, in cases where the whole face is not visible, the FPA detection performance of other parts of the face has also been examined. Therefore, in this study, FPA detection performances of facial regions of wide face, cropped face, eyes, nose, and mouth was investigated. For this purpose, the facial regions were determined and normalized, and texture features were extracted using powerful texture descriptor local binary patterns (LBP) due to its easy computability and low processing complexity. Multi-block LBP features are used to obtain more detailed texture information. Generally uniform LBP patterns are used for feature extraction in the literature. In this study, the FPA detection performances of both uniform LBP patterns and all LBP patterns were investigated. The size of feature vector is reduced by principal component analysis, and real/fake classification is performed with support vector machines. Experimental results on NUAA, CASIA, REPLAY-ATTACK and OULU-NPU datasets show that the use of all patterns increased the performance of FPA detection.

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Section: Related Workmentioning

confidence: 99%

Face presentation attack detection performances of facial regions with multi-block LBP features

Yılmaz

Turhal

Nabiyev

2023

Multimed Tools Appl

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“…The obtained detection accuracy is 82.66%. This is because LBP and GLCM have a prohibitively high potential for performing image spoofing [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of capsicum leaf disease from a complex cluster of leaves using an improved multiple layers ShuffleNet CNN model

2023

IJATEE

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Capsicum, also known as chili pepper or bell pepper, is cultivated worldwide and holds significant economic importance as a condiment, vegetable, and medicinal plant. One of the major challenges in capsicum cultivation is the accurate identification of leaf diseases. Leaf diseases can have a detrimental effect on the quality of capsicum production, leading to substantial losses for farmers. Several machine learning (ML) algorithms and convolutional neural network (CNN) models have been developed to classify capsicum leaf diseases under controlled conditions, where leaves are uniform and backgrounds are uncomplicated. These models have achieved an average accuracy of classification. However, classifying diseases becomes relatively challenging when a diseased leaf grows alongside a cluster of other leaves. Having a reliable model that can accurately classify capsicum leaf diseases within a cluster of leaves would greatly benefit farmers. Therefore, the aim of this study was to propose a model capable of classifying capsicum leaf diseases both from a uniform background and within a complex cluster of leaves. Firstly, a dataset comprising images of diseased capsicum leaves, including discolored leaves, grey spots, and leaf curling, was acquired. Subsequently, an improved multiple-layer ShuffleNet CNN model was employed to classify the different types of capsicum leaf diseases. The proposed model demonstrated superior performance compared to existing models, achieving a classification accuracy of 99.30%.Furthermore, it was concluded that augmenting the layers of ShuffleNet, utilizing a 0.01 initial learning rate, employing 50 maximum epochs, using a minibatch size of 64, conducting 10 iterations, and incorporating 205 validation iterations all contributed to the improved ShuffleNet model's success.

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“…Terlebih wajah seseorang jauh lebih mudah didapat dibandingkan biometrik lainnya seperti sidik jari. Wajah seseorang bisa dengan mudah didapatkan melalui foto profil seseorang di media sosial [2].…”

Section: Pendahuluanunclassified

Deteksi Serangan Spoofing Wajah Menggunakan Convolutional Neural Network

Hadiprakoso¹,

Buana²

2021

JuTISI

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Facial recognition-based biometric authentication is increasingly frequently employed. However, a facial recognition system should not only recognize an individual's face, but it should also be capable of detecting spoofing attempts using printed faces or digital photographs. There are now various methods for detecting spoofing, including blinking, lip movement, and head tilt detection. However, this approach has limitations when dealing with dynamic video spoofing assaults. On the other hand, these types of motion detection systems can diminish user comfort. As a result, this article presents a method for identifying facial spoofing attacks through Convolutional Neural Networks. The anti-spoofing technique is intended to be used in conjunction with deep learning models without using extra tools or equipment. Our CNN classification dataset can be derived from the NUAA photo imposter and CASIA v2. The collection contains numerous examples of facial spoofing, including those created with posters, masks, and smartphones. In the pre-processing stage, image augmentation is carried out with brightness adjustments and other filters so that the model to adapt to various environmental conditions. We evaluate the number of epochs, optimizer types, and the learning rate during the testing process. The test results show that the proposed model achieves an accuracy value of 91.23% and an F1 score of 92.01%.

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Spoofing detection on facial images recognition using LBP and GLCM combination

Cited by 19 publications

References 0 publications

Face presentation attack detection performances of facial regions with multi-block LBP features

Face presentation attack detection performances of facial regions with multi-block LBP features

Classification of capsicum leaf disease from a complex cluster of leaves using an improved multiple layers ShuffleNet CNN model

Deteksi Serangan Spoofing Wajah Menggunakan Convolutional Neural Network

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