Face Recognition Using DRLBP and SIFT Feature Extraction

Sushama, M.; Rajinikanth, E.

doi:10.1109/iccsp.2018.8524427

Cited by 14 publications

(5 citation statements)

References 4 publications

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“…Visual SLAM localization methods include feature-based methods, direct methods, and depth learning-based methods. Feature-based SLAM utilizes feature methods, such as SIFT [62], SURF [63], and ORB [64], to extract feature points by calculating algorithms that match feature points between adjacent frames. By leveraging geometric relationships, it obtains the rotation matrix and translation matrix of the camera, thus determining the camera's pose.…”

Section: Visual Slam Localizationmentioning

confidence: 99%

A Review of Underwater Robot Localization in Confined Spaces

Wu,

Chen,

Yang

et al. 2024

JMSE

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Underwater robots often encounter the influence of confined underwater environments during underwater exploration. These environments include underwater caves, sunken ships, submerged houses, and pipeline structures. Robot positioning in these environments is strongly disturbed, leading not only to the failure of some commonly used positioning methods but also to an increase in errors in positioning systems that normally function well in open water. In order to overcome the limitations of positioning methods in confined underwater environments, researchers have studied different underwater positioning methods and have selected suitable methods for positioning in such environments. These methods can achieve high-precision positioning without relying on assistance from other platforms and are referred to as autonomous positioning methods. Autonomous positioning methods for underwater robots mainly include SINS/DR positioning and SLAM positioning. In addition, in recent years, researchers have developed some bio-inspired autonomous positioning methods. This article introduces applicable robot positioning methods and sensors in confined underwater environments and discusses the research directions of robot positioning methods in such environments.

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Section: Visual Slam Localizationmentioning

confidence: 99%

A Review of Underwater Robot Localization in Confined Spaces

Wu,

Chen,

Yang

et al. 2024

JMSE

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“…There are many existing point features extraction algorithms, such as scale invariant feature transform (SIFT) [ 19 ], speeded up robust features (SURF) [ 20 ], oriented FAST and rotated BRIEF (ORB) [ 21 ]. SIFT and SURF have more accurate feature extraction results, but they are too time-consuming [ 22 , 23 , 24 ].…”

Section: Related Workmentioning

confidence: 99%

Improved Point-Line Feature Based Visual SLAM Method for Complex Environments

Zhou

Zhang

Deng

et al. 2021

Sensors

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Traditional visual simultaneous localization and mapping (SLAM) systems rely on point features to estimate camera trajectories. However, feature-based systems are usually not robust in complex environments such as weak textures or obvious brightness changes. To solve this problem, we used more environmental structure information by introducing line segments features and designed a monocular visual SLAM system. This system combines points and line segments to effectively make up for the shortcomings of traditional positioning based only on point features. First, ORB algorithm based on local adaptive threshold was proposed. Subsequently, we not only optimized the extracted line features, but also added a screening step before the traditional descriptor matching to combine the point features matching results with the line features matching. Finally, the weighting idea was introduced. When constructing the optimized cost function, we allocated weights reasonably according to the richness and dispersion of features. Our evaluation on publicly available datasets demonstrated that the improved point-line feature method is competitive with the state-of-the-art methods. In addition, the trajectory graph significantly reduced drift and loss, which proves that our system increases the robustness of SLAM.

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“…This technique was tested to detect multiple faces in a single detection process. Dominant Rotated Local Binary Pattern (DRLBP) and Scale Invariant Feature Transform (SIFT) based facial recognition system is proposed in Literature [5].Here the face image is preprocessed before extracting the features by applying DRLBP and SIFT. The extracted features are compared using the Back Propagation Network (BPN).…”

Section: Literature Surveymentioning

confidence: 99%

“…From the previous work of face recognition models [2,3,4,5,6,7,12,13], it is observed that the spatial feature domain alone affects the performance of the system, since it produces spectral degradation because of not having fixed set basis vectors. Further, the traditional works are demonstrated using minimal database for performance evaluation.…”

Section: Literature Surveymentioning

confidence: 99%

Performance Evaluation of Face Recognition system by Concatenation of Spatial and Transformation Domain Features

K¹,

Ravi²

2021

IJCNIS

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Face biometric system is one of the successful applications of image processing. Person recognition using face is the challenging task since it involves identifying the 3D object from 2D object. The feature extraction plays a very important role in face recognition. Extraction of features both in spatial as well as frequency domain has more advantages than the features obtained from single domain alone. The proposed work achieves spatial domain feature extraction using Asymmetric Region Local Binary Pattern (ARLBP) and frequency domain feature extraction using Fast Discrete Curvelet Transform (FDCT). The obtained features are fused by concatenation and compared with trained set of features using different distance metrics and Support Vector Machine (SVM) classifier. The experiment is conducted for different face databases. It is shown that the proposed work yields 95.48% accuracy for FERET, 92.18% for L-space k, 76.55% for JAFFE and 81.44% for NIR database using SVM classifier. The results show that the proposed system provides better recognition rate for SVM classifier when compare to the other distance matrices. Further, the work is also compared with existing work for performance evaluation.

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Face Recognition Using DRLBP and SIFT Feature Extraction

Cited by 14 publications

References 4 publications

A Review of Underwater Robot Localization in Confined Spaces

A Review of Underwater Robot Localization in Confined Spaces

Improved Point-Line Feature Based Visual SLAM Method for Complex Environments

Performance Evaluation of Face Recognition system by Concatenation of Spatial and Transformation Domain Features

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