Local Binary Pattern Based Feature Extraction with KELM for Face Identification

Ahuja, Bhawna; Vishwakarma, Virendra P.

doi:10.1109/icsc48311.2020.9182760

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…Finally, concentrate on the third approach, which is a criminal identification module that will detect face recognition input during face anti-spoofing detection. If a real face is found in the face anti-spoofing detection module, this face provides input to the criminal identification module, which determines whether the face is normal or criminal [18]- [20]. The methodology we propose is made up of several general steps.…”

Section: Proposed Workmentioning

confidence: 99%

Face and liveness detection with criminal identification using machine learning and image processing techniques for security system

Shinde,

Raundale

2024

IJ-AI

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<p>In the past, real-world photos have been used to train classifiers for face liveness identification since the related face presentation attacks (PA) and real-world images have a high degree of overlap. The use of deep convolutional neural networks (CNN) and real-world face photos together to identify the liveness of a face, however, has received very little study. A face recognition system should be able to identify real faces as well as efforts at faking utilizing printed or digital presentations. A true spoofing avoidance method involves observing facial liveness, such as eye blinking and lip movement. However, this strategy is rendered useless when defending against replay assaults that use video. The anti-spoofing technique consists of two modules: the ConvNet classifier module and the blinking eye module, which measure lip and eye movement. The results of the testing demonstrate that the developed module is capable of identifying various face spoof assaults, including those made with the use of posters, masks, or smartphones. To assess the convolutional features in this study adaptively fused from deep CNN produced face pictures and convolutional layers learned from real-world identification. Extensive tests using intra-database and cross-database scenarios on cutting-edge face anti-spoofing databases including CASIA, OULU, NUAA and replay-attack dataset demonstrate that the proposed solution methods for face liveness detection. The algorithm has a 94.30% accuracy rate.</p>

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Section: Proposed Workmentioning

confidence: 99%

Face and liveness detection with criminal identification using machine learning and image processing techniques for security system

Shinde,

Raundale

2024

IJ-AI

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“…These techniques were robust and efficient for face recognition. Moreover, these techniques have been modified and developed several times [15] for example, LBP has been modified to be more efficient in face recognition [16].…”

Section: Face Recognitionmentioning

confidence: 99%

New Deep Learning Model for Face Recognition and Registration in Distance Learning

Salamh¹,

Akyüz²

2022

Int. J. Emerg. Technol. Learn.

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The demand for secure, accurate, and reliable identification of individuals using facial recognition has attracted considerable interest in education, security, and many other sectors, not limited because it is robust, secure, and authentic. Recently, the demand for distance learning has increased dramatically. This increase is due to various barriers to learning that arise from enforced conditions such as seclusion and social distancing. Facial feature extraction in distance education is valuable in supporting face authenticity as it prevents the position of participants from changing, especially during the examination phase. In the field of face recognition, there is a mismatch between research and practical application. In this paper, we present a novel but highly efficient Deep Learning model for improving face recognition and registration in distance education. The technique is based on a combination of sequential and residual identity blocking. This makes it possible to evaluate the effectiveness of using deeper blocks than other models. The new model has proven to be able to extract features from faces in a high and accurate manner in compared with other state-of-the-art methods. In registration processing, there are several challenges related to training data limitation, face recognition, and verification. We present a new architecture for face recognition and registration. Experiments have shown that our registration model is capable of recognizing almost all faces and registering the corresponding labels.

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Deterministic multikernel extreme learning machine with fuzzy feature extraction for pattern classification

Ahuja

Vishwakarma

2021

Multimed Tools Appl

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Local Binary Pattern Based Feature Extraction with KELM for Face Identification

Cited by 6 publications

References 23 publications

Face and liveness detection with criminal identification using machine learning and image processing techniques for security system

Face and liveness detection with criminal identification using machine learning and image processing techniques for security system

New Deep Learning Model for Face Recognition and Registration in Distance Learning

Deterministic multikernel extreme learning machine with fuzzy feature extraction for pattern classification

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