Effective and Fast Face Recognition System Using Complementary OC-LBP and HOG Feature Descriptors With SVM Classifier

Chhabra, Indu; Singh, Geetika

doi:10.4018/jitr.2018010106

Cited by 18 publications

(10 citation statements)

References 49 publications

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“…Considering the detailed description of the shipwreck texture in local space and frequency domain information, Gabor texture features were extracted to further enrich the texture representation of the shipwreck target. Considering that the SSS waterfall image target recognition requires multi-scale processing of the image, the local binary pattern (LBP) features are extracted to characterize multi-scale features; it is an operator used to describe the local features of SSS images, and LBP features have significant advantages such as gray and rotation invariance [34].…”

Section: Shipwreck Target Recognition Model Constructionmentioning

confidence: 99%

Active Learning for Recognition of Shipwreck Target in Side-Scan Sonar Image

et al. 2019

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In order to realize the automatic and accurate recognition of shipwreck targets in side-scan sonar (SSS) waterfall images, a pipeline that contains feature extraction, selection, and shipwreck recognition, an AdaBoost model was constructed by sample images. Shipwreck targets are detected quickly by a nonlinear matching model, and a shipwreck recognition in SSS waterfall images are given, and according to a wide set of combinations of different types of these individual procedures, the model is able to recognize the shipwrecks accurately. Firstly, two feature-extraction methods suitable for recognizing SSS shipwreck targets from natural sea bottom images were studied. In addition to these two typical features, some commonly used features were extracted and combined as comprehensive features to characterize shipwrecks from various feature spaces. Based on Independent Component Analysis (ICA), the preferred features were selected from the comprehensive features, which avoid dimension disaster and improved the correct recognition rate. Then, the Gentle AdaBoost algorithm was studied and used for constructing the shipwreck target recognition model using sample images. Finally, a shipwreck target recognition process for the SSS waterfall image was given, and the process contains shipwreck target fast detection by a nonlinear matching model and accurate recognition by the Gentle AdaBoost recognition model. The results show that the correct recognition rate of the model for the sample image is 97.44%, while the false positive rate is 3.13% and the missing detection rate is 0. This study of a measured SSS waterfall image confirms the correctness of the recognition process and model.

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Section: Shipwreck Target Recognition Model Constructionmentioning

confidence: 99%

Active Learning for Recognition of Shipwreck Target in Side-Scan Sonar Image

et al. 2019

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“…Recent advances in HOG feature extraction methods have facilitated investigation of face recognition. In Singh & Chhabra's (2018) article, she suggests that HOG features can be used in the recognition system for improving the efficiency and the processing speed. The function of HOG features can capture the edge features that are invariant to the rotation and light.…”

Section: Related Workmentioning

confidence: 99%

3D texture-based face recognition system using fine-tuned deep residual networks

Zheng

Rahmat

Khalid

et al. 2019

PeerJ Computer Science

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As the technology for 3D photography has developed rapidly in recent years, an enormous amount of 3D images has been produced, one of the directions of research for which is face recognition. Improving the accuracy of a number of data is crucial in 3D face recognition problems. Traditional machine learning methods can be used to recognize 3D faces, but the face recognition rate has declined rapidly with the increasing number of 3D images. As a result, classifying large amounts of 3D image data is time-consuming, expensive, and inefficient. The deep learning methods have become the focus of attention in the 3D face recognition research. In our experiment, the end-to-end face recognition system based on 3D face texture is proposed, combining the geometric invariants, histogram of oriented gradients and the fine-tuned residual neural networks. The research shows that when the performance is evaluated by the FRGC-v2 dataset, as the fine-tuned ResNet deep neural network layers are increased, the best Top-1 accuracy is up to 98.26% and the Top-2 accuracy is 99.40%. The framework proposed costs less iterations than traditional methods. The analysis suggests that a large number of 3D face data by the proposed recognition framework could significantly improve recognition decisions in realistic 3D face scenarios.

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“…The local methods are too effective than the global for face recognition, even if it is a minute feature (Singh and Chabra 2018 ). Several local feature extraction methods, such as local binary pattern (LBP), local ternary pattern (LTD), local derivative pattern (LDP), Weber’s local descriptor (WLB), histogram of oriented gradient (HOG), have been proposed by many researchers (Shi et al 2010 ; Tan and Triggs 2010 ; Chai et al 2014 ; Singh and Chabra 2018 ; He et al 2018 ; Muqeet and Holambe 2019 ) for face recognition. They have reported that the local feature extraction methods yield better results than the global methods.…”

Section: Introductionmentioning

confidence: 99%

A hybrid method for biometric authentication-oriented face detection using autoregressive model with Bayes Backpropagation Neural Network

Vasanthi

Seetharaman

2021

Soft Comput

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This paper proposes a novel method, which is coined as ARBBPNN, for biometric-oriented face detection, based on autoregressive model with Bayes backpropagation neural network (BBPNN). Firstly, the given input colour key face image is modelled to HSV and YCbCr models. A hybrid model, called HS-YCbCr, is formulated based on the HSV and YCbCr models. The submodel, H, is divided into various sliding windows of size, 3 9 3. The model parameters are estimated for the window using the BBPNN. Based on the model coefficients, autocorrelation coefficients (ACCs) are computed. An autocorrelation test tests the significance of the ACCs. If the ACC passes the test, then it is inferred that the small image region, viz. the window, represents the texture and it is treated as the texture feature. Otherwise, it is regarded as structure, which is treated as the shape feature. The texture and shape features are formulated as feature vectors (FV) separately, and they are combined into a single FV. This process is performed for all colour submodels. The FVs of the submodels are combined into a single holistic vector, which is treated as the FV of the key face image. The key FV has twenty feature elements. The similarity of the key and target face images is examined, based on the key and target FVs, by deploying multivariate parametric statistical tests. If the FVs of the key and target images pass the tests, then it is concluded that the key and target face images are the same; otherwise, they are regarded as different. The GT, FSW, Pointing'04, and BioID datasets are considered for the experiments. In addition to the above datasets, we have constructed a dataset with face images collected from Google, and many images captured through a digital camera. It is also subjected to the experiment. The obtained recognition results show that the proposed ARBBPNN method outperforms the existing methods.

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Effective and Fast Face Recognition System Using Complementary OC-LBP and HOG Feature Descriptors With SVM Classifier

Cited by 18 publications

References 49 publications

Active Learning for Recognition of Shipwreck Target in Side-Scan Sonar Image

Active Learning for Recognition of Shipwreck Target in Side-Scan Sonar Image

3D texture-based face recognition system using fine-tuned deep residual networks

A hybrid method for biometric authentication-oriented face detection using autoregressive model with Bayes Backpropagation Neural Network

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