The ear recognition system is an attractive research topic in the area of biometrics. It involves building machine learning models to verify the identities of humans using their ears. In this article, an exploration o...
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Watermarking scheme is an efficient solution to protect the multimedia document from unauthorized modification. Several watermarking scheme have been developed but authentication and tamper detection is still an impor...
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Watermarking scheme is an efficient solution to protect the multimedia document from unauthorized modification. Several watermarking scheme have been developed but authentication and tamper detection is still an important research issue. In this paper, a dual image-based watermarking scheme has been developed using local binary pattern (LBP) to protect the multimedia document from illegal modification. The suggested method includes the following procedure: During watermark encoding the host image is partitioned into (3 x 3) nonoverlapping blocks. Then generate system vector (s) using LBP and perform XOR with secret watermark bits. Two bits authentication code is generated from S vector and embed within dual image depending in a shared secret key (delta). At the receiver end, the embedded watermark, authentication code, and original cover image can be successfully recovered from dual watermarked image. After that authentication process has been carried out by comparing extracted authentication code and the regenerated authentication code. The experimental results are compared with the state-of-the-art methods to show the effectiveness of our proposed scheme. It is observed that the proposed scheme is secure and robust against different standard attacks meanwhile it can detect message integrity within the watermarked object.
Characteristic extraction in face recognition is a step to get characteristic information from the image. The characteristic extraction algorithm is tested against several scenarios of different sunlight and lights, o...
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Characteristic extraction in face recognition is a step to get characteristic information from the image. The characteristic extraction algorithm is tested against several scenarios of different sunlight and lights, objects facing the camera and not facing the camera. The sample test data were performed on 4 people using a video file or frame numbering 70 for recognizable faces using Principal Component Analysis (PCA) and local binary pattern (LBP) algorithms. The result of the research shows that local binary pattern (LBP) algorithm in object scenario facing camera with sunlighting in room has accuracy of 98.59%, recognition time of 812,817 milliseconds, FAR of 1,41% and FRR of 0%, while at Principal Component Analysis (PCA) 98.59% accuracy, recognition time of 1275,761 milliseconds, FAR of 1.41% and FRR of 0%. Based on these results, the local binary pattern (LBP) algorithm is more efficient than Principal Component Analysis (PCA) for face recognition of the scenarios to be implemented in real-time video.
Due to computational simplicity and outstanding ability of one dimensional local binary pattern (1DLBP) to capture the most representative structures of 1D signals, this operator has been recently exploited for featur...
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ISBN:
(纸本)9781538695692
Due to computational simplicity and outstanding ability of one dimensional local binary pattern (1DLBP) to capture the most representative structures of 1D signals, this operator has been recently exploited for feature extraction from biological signals. The original version of 1DLBP is obtained by first order derivative of signal and reveals its changes in time. We have improved the concept and introduced one dimensional second order derivative local binary pattern which better reveals signal changes and also exhibits convexities and concavities of the signal in time. The proposed operator has been utilized for feature extraction from EMG signals of sEMG for basic hand movement dataset and SVM has been used to classify the extracted features. The best classification accuracy of 94.9% was obtained using the combination of the first and second order derivatives. Experiments demonstrate the efficacy of the proposed feature extraction method compared to other prevalent approaches.
One of the areas on the human body that has the most dominant racial trait is the face. This research build the classification system for Mongoloid and non-Mongoloid race based on the area in the periorbital area of f...
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ISBN:
(纸本)9781538645727
One of the areas on the human body that has the most dominant racial trait is the face. This research build the classification system for Mongoloid and non-Mongoloid race based on the area in the periorbital area of facial image. We use local binary pattern to extract texture features on periorbital facial area. To classify the LBP features, we use Support Vector Machine classifier. The accuracy obtained from the system is 99.38%.
In this paper, a method combining local binary pattern(LBP) and Support Vector Machine(SVM) for smile detection is proposed. The process of smile recognition is divided into 5 parts including input images, image enfor...
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ISBN:
(纸本)9781538670576
In this paper, a method combining local binary pattern(LBP) and Support Vector Machine(SVM) for smile detection is proposed. The process of smile recognition is divided into 5 parts including input images, image enforcement, face detection, feature extraction and classification. Firstly, the face images are downloaded from the Japanese Female Facial Expression(JAFFE) Database, which is then followed by the process of the image enforcement and processing such as noise removing and image normalization. After this, the human face extraction algorithm based on the combination of Haar features and cascading AdaBoost algorithm is used to segment the human face from the images. Furthermore, local binary pattern(LBP) is applied to extract features from face images. Finally, Support Vector Machine based on Sequential Minimum Optimization(SMO) algorithm is implemented to classify the input feature vectors into two categories smiling images or not smiling images. The result shows that this method can get the accuracy of 88.1%.
Wood is one of Indonesia's very rich natural resources abundant because the number reaches around 4,000 species. The process of identifying wood species currently it is still done manually in a relatively long tim...
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ISBN:
(纸本)9781538657416
Wood is one of Indonesia's very rich natural resources abundant because the number reaches around 4,000 species. The process of identifying wood species currently it is still done manually in a relatively long time by observing types of fibers, vessels, rays, and other structures directly because there is not a much automatic application of identification of wood species is made. This is an obstacle for experts anatomy of wood because it must check wood species accurately and quickly. Therefore that, the field of Computer Vision is the right solution to develop the process Identification of wood species automatically. In this research program will be made application of Computer Vision to identify wood species with using the Daubechies Wavelet (DW) and local binary pattern (LBP) methods for The extraction of the wood pattern is then classified Support Vector Machine (SVM) method. Results obtained in this study is able to identify the microscopic image of wood as a species of wood with average SVM accuracy is 85%.
The emergence of low cost digital cameras and other image capturing devices has created a huge amount of different types of images. Accessing images easily requires proper arrangement and indexing of images. This has ...
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ISBN:
(纸本)9783319754208;9783319754192
The emergence of low cost digital cameras and other image capturing devices has created a huge amount of different types of images. Accessing images easily requires proper arrangement and indexing of images. This has made image retrieval an important problem of Computer Vision. This paper attempts to decompose a local binary pattern (LBP) image at multiple resolution to extract structural arrangement of pixels more efficiently than processing a single scale of the LBP image. LBP descriptors of the 2-D gray scale image are computed followed by computation of Discrete Wavelet Transform (DWT) coefficients of the resulting 2-D LBP image. Finally, construction of feature vector is done through Gray-Level Co-occurrence Matrix. Performance of the proposed method is tested on two benchmark datasets, Corel-1K and Corel-5K, and measured in terms of Precision and Recall. The experimental results demonstrate that the proposed method outperforms some of the other state-of-the-art methods, which proves the effectiveness of the proposed method.
In this paper, we propose a new mammogram classification scheme to classify the breast tissues as normal or abnormal. Feature matrix is generated using local binary pattern to all the detailed coefficients from 2D-DWT...
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In this paper, we propose a new mammogram classification scheme to classify the breast tissues as normal or abnormal. Feature matrix is generated using local binary pattern to all the detailed coefficients from 2D-DWT of the region of interest (ROI) of a mammogram. Feature selection is done by selecting the relevant features that affect the classification. Feature selection is used to reduce the dimensionality of data and features that are not relevant, in this paper the F-test and Ttest will be performed to the results of the feature extraction dataset to reduce and select the relevant feature. The best features are used in a Neural Network classifier for classification. In this research we use MIAS and DDSM database. In addition to the suggested scheme, the competent schemes are also simulated for comparative analysis. It is observed that the proposed scheme has a better say with respect to accuracy, specificity and sensitivity. Based on experiments, the performance of the proposed scheme can produce high accuracy that is 92.71%, while the lowest accuracy obtained is 77.08%.
Human action recognition from the videos is one of the most attractive topics in computer vision during the last decades due to wide applications development. This research has mainly focused on learning and recognizi...
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ISBN:
(纸本)9788086943428
Human action recognition from the videos is one of the most attractive topics in computer vision during the last decades due to wide applications development. This research has mainly focused on learning and recognizing actions from RGB and Depth videos (RGBD). RGBD is a powerful source of data providing the aligned depth information which has great ability to improve the performance of different problems in image understanding and video processing. In this work, a novel system for human action recognition is proposed to extract distinctive spatio and temporal feature vectors for presenting the spatio-temporal evolutions from a set of training and testing video sequences of different actions. The feature vectors are computed in two steps: The First step is the motion detection from all video frames by using spatio-temporal retina model. This model gives a good structuring of video data by removing the noise and illumination variation and is used to detect potentially salient areas, these areas represent the motion information of the moving object in each frame of video sequences. In the Second step, because of human motion can be seen as a type of texture pattern, the local binary pattern descriptor (LBP) is used to extract features from the spatio-temporal salient areas and formulated them as a histogram to make the bag of feature vectors. To evaluate the performance of the proposed method, the k-means clustering, and Random Forest classification is applied on the bag of feature vectors. This approach is demonstrated that our system achieves superior performance in comparison with the state-of-the-art and all experimental results are depending on two public RGBD datasets.
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