Biometric Facial Detection and Recognition Based on ILPB and SVM

Srivastava, Shubhi; Kumar, Ankit; Prakash, Shiv

doi:10.1002/9781119760429.ch8

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…(1) e theory is simple and clear and easy to understand, the calculation process is convenient and fast, and it is very easy to realize from the perspective of coding [10]. To sum up, ILBP-based methods require only simple template operation and histogram vector generation.…”

Section: Advantages Of Ilbp Algorithmmentioning

confidence: 99%

Image Recognition about Stability of Soft Surrounding Rock in Tunnel Based on ILBP Algorithm

Lang

Gao

Jia

et al. 2022

Security and Communication Networks

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With China’s fast expansion of urban traffic and railway engineering, a great number of subway and railway lines have been erected in subterranean tunnels in recent years. When the tunnel is in the process of construction, it will encounter the weak fracture zone, and the surrounding rock of this part of the tunnel is often poor stability and deformation is difficult to control, which will lead to the smooth progress of the project, delay and other problems. Therefore, to facilitate the timely treatment of tunnel soft surrounding rock, the study of image recognition of its stability is particularly important. Based on this, an improved local binary mode algorithm (ILBP) is proposed in this paper. By adjusting the weight of binary polynomial in local binary mode (LBP) operator, the target of ILBP is to extract the characteristics of weak surrounding rock of tunnel at a specific location, hoping to provide reference for the follow-up study of tunnel engineering.

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Section: Advantages Of Ilbp Algorithmmentioning

confidence: 99%

Image Recognition about Stability of Soft Surrounding Rock in Tunnel Based on ILBP Algorithm

Lang

Gao

Jia

et al. 2022

Security and Communication Networks

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“…The most popular (SVM) is mostly useful among rationalizing a dependable technique which can be cataloging improved facemask characteristics, here is generally analyzed based over the balanced value and SVM picks out the hyperplane to categorize the discriminant investigation structures [18]. While carrying out the face recognition task multiple factors are dependent like distinct view angle, dimensions, position of object, emotion on face, crowd intensity, multiple object appearance in one frame, radiance etc.…”

mentioning

confidence: 99%

“…Such a variety of face image or video input having these assemblages also go inside. Unattractive random changes, noised or filtered images, and lowquality pixels [18] [25]. Though object chasing and discovery is a high priority task, dynamic frequently crowd in fame tough task because of different angle and position view of capture, high person compactness and fluctuation in person appearance these factors study in some recent years [1].…”

mentioning

confidence: 99%

Tri Bird Technique for Effective Face Recognition Using Deep Convolutional Neural Network

Musale,

Singh,

Shirke

et al. 2023

Atlantis Highlights in Computer Sciences

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The massive amount of data produced from Video content, has speedily grown up with surveillance data. Human beings can easily handle this data by greatly outperforming the capacity of technical and non-technical resources. In the current era of computer vision great characteristics like precision, effectiveness and interoperability of smart surveillance systems to retrieve data. Security surveillance systems rapidly increase Face value acknowledgement over different subsequent critical methods. In proposed research, deep CNN and deep BiLSTM methods were used for effective face recognition from video input. Generally, the said techniques efficiently handle challenges face recognition by combining the recompenses of computer vision and tri bird optimization. The proposed method can be used in many computer vision applications that help recognize faces. The hybrid deep model is developed using Distributed Deep CNN classifier and distributed BiLSTM classifier, which is optimized using the new optimization that is designed newly for your work. The new optimization is designed based on the characters of vulture, sparrow and crow. Then the output of the classifier is fed through the model by analyzing the face query to attain the retrieved relevant face. The outcome can be measured by enactment over the proposed supposed model and is analyzed through considering metrics such as accuracy, exactness, remembrance and F-value calculator which are implemented in the MATLAB tool to reveal the proposed face recognition method. The proposed model achieves 91.30% accuracy, 95.07% precision, 95.078% recall and 95.078% f-measures by deep convolutional neural network.

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Identification of psychological stress from speech signal using deep learning algorithm

Kumar,

Shaun,

Chaurasia

2024

e-Prime - Advances in Electrical Engineering, Electronics and E

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Biometric Facial Detection and Recognition Based on ILPB and SVM

Cited by 3 publications

References 12 publications

Image Recognition about Stability of Soft Surrounding Rock in Tunnel Based on ILBP Algorithm

Image Recognition about Stability of Soft Surrounding Rock in Tunnel Based on ILBP Algorithm

Tri Bird Technique for Effective Face Recognition Using Deep Convolutional Neural Network

Identification of psychological stress from speech signal using deep learning algorithm

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