Center-symmetric Local Binary Pattern Features Research Articles

Intramuscular EMG classifier for detecting myopathy and neuropathy

This article presents an automatic diagnostic system to classify intramuscular electromyography (iEMG) signals, thereby detecting neuromuscular disorders. To this end, we tailored the center symmetric local binary pattern (CSLBP) to analyze one-dimensional ( 1 -D) signals. In this approach, the 1 -D CSLBP feature extracted from a decimated iEMG signal is fed to a combination of classifiers, which in turn assigns a set of labels to the signal, and ultimately the signal category is determined by the Boyer-Moore majority voting (BMMV) algorithm. The proposed framework was investigated with a benchmark iEMG dataset that contains signals recorded from three different muscles: biceps brachii (BB), deltoideus (DE), and vastus medialis (VM). In a repeated 10 -fold cross-validation, CSLBP-Combined-Classifiers-BMMV (CSLBP-CC-BMMV) achieved an average classification accuracy of 92.80 %, 94.25 %, and 93.71 % for the iEMG signals recorded from BB, DE, and VM muscle, respectively. Interestingly, the performance of CSLBP-CC-BMMV surpassed the other published approaches and ensemble learning methods that are akin to our scheme in terms of classification accuracy and computational time.

Smoke-Detection Framework for High-Definition Video Using Fused Spatial- and Frequency-Domain Features

Video-based smoke detection is an effective method for fire alarm systems. Given the widespread use of high-definition cameras, a smoke detection method for high-definition video is needed. This paper proposes a smoke-detection framework for high-definition video, in which the main idea is to use the small smoke image blocks to match the image features of the motion area in the video and to use the support vector machine classifier for smoke recognition. The ViBe algorithm and other methods are used to effectively extract the areas for classification. This detection framework consists of spatial- and frequency-domain features. In the extraction of frequency domain features, we use local phase quantization (LPQ) features. In the local texture features of the spatial domain, we add the compensation of adjacent pixels and consider the gradient of the symmetrical pixels using the center-symmetric local binary pattern feature. To improve results, we also propose the trisection feature fusion scheme for features in the spatial and frequency domains. The experiments show that using the feature extraction and fusion schemes, our smoke-detection framework achieves the good performance in the detection of smoke in the video from different datasets.

Single-sensor hand-vein multimodal biometric recognition using multiscale deep pyramidal approach

Biometrics has emerged as a powerful technology for person authentication in various scenarios including forensic and civilian applications. Deployment of biometric solutions that use cues from multiple modalities enhances the reliability and robustness of authentication necessary to meet the increasingly stringent security requirements. However, there are two drawbacks typically associated with multimodal biometrics. Firstly, the image acquisition process in such systems is not very user-friendly, primarily due to the time and effort required to capture biometric samples belonging to multiple modalities. Secondly, the overall cost is higher as they employ multiple biometric sensors. To overcome these drawbacks, we employ a single NIR sensor-based image acquisition in the proposed approach for hand-vein recognition. From the input hand image, a palm-vein and four finger-vein subimages are extracted. These images are then enhanced by CLAHE and transformed into illumination invariant representation using center-symmetric local binary pattern (CS-LBP). Further, a hierarchical non-rigid matching technique inspired by the architecture of deep convolutional networks is employed for matching the CS-LBP features. Finally, weighted sum rule-based matching score-level fusion is performed to combine the palm-vein and the four finger-vein modalities. A set of rigorous experiments has been performed on an in-house database collected from the left and right hands of 185 subjects and the publicly available CASIA dataset. The proposed approach achieves equal error rates of 0.13% and 1.21%, and rank-1 identification rates of 100% and 100% on the in-house and CASIA datasets, respectively. Additionally, we compare the proposed approach with the state-of-the-art techniques proposed for vascular biometric recognition in the literature. The important findings are (1) the proposed approach outperforms all the existing techniques considered in this study, (2) the fusion of palm-vein and finger-vein modalities consistently leads to better performance for all the feature extraction techniques considered in this work. (3) Furthermore, our experimental results also suggest that considering the constituent palm-vein and finger-vein images instead of the entire hand-vein images achieves better performance.

Local binary pattern variants-based adaptive texture features analysis for posed and nonposed facial expression recognition

Facial expression recognition (FER) is an important task for various computer vision applications. The task becomes challenging when it requires the detection and encoding of macro- and micropatterns of facial expressions. We present a two-stage texture feature extraction framework based on the local binary pattern (LBP) variants and evaluate its significance in recognizing posed and nonposed facial expressions. We focus on the parametric limitations of the LBP variants and investigate their effects for optimal FER. The size of the local neighborhood is an important parameter of the LBP technique for its extraction in images. To make the LBP adaptive, we exploit the granulometric information of the facial images to find the local neighborhood size for the extraction of center-symmetric LBP (CS-LBP) features. Our two-stage texture representations consist of an LBP variant and the adaptive CS-LBP features. Among the presented two-stage texture feature extractions, the binarized statistical image features and adaptive CS-LBP features were found showing high FER rates. Evaluation of the adaptive texture features shows competitive and higher performance than the nonadaptive features and other state-of-the-art approaches, respectively.

A cloud-based monitoring system via face recognition using Gabor and CS-LBP features

Face detection and recognition is an important topic in security. Currently, ubiquitous monitoring has received a large amount of attention. This paper proposes a cloud-based ubiquitous monitoring system via face recognition. It consists of a monitoring client module for face detection and recognition and a cloud storage module for data visualization. In the monitoring client module, the center-symmetric local Gabor binary pattern feature extraction method is proposed for face recognition, which combines improved multi-scale Gabor and center-symmetric local binary pattern (CS-LBP) features. This method maintains crucial local features, reduces the Gabor filter complexity, and adds rotational invariance and more precise texture information. A large number of experiments on the ORL, Yale-B, and Yale databases show that the proposed method obtains significantly better recognition rates than the LBP, CS-LBP, and Scale Gabor methods. Furthermore, we propose a Web browser-based data visualization that renders the geographic locations of the face detection and recognition results.

Sparse Hierarchical Clustering for VHR Image Change Detection

The traditional clustering approaches are limited for the unsupervised change detection of very high resolution images due to the multimodal distribution of change features. To overcome this difficulty, a sparse hierarchical clustering approach is proposed. Discriminative change features are generated by stacking bitemporal multiscale center-symmetric local binary pattern features. In order to explore the multimodal and hierarchical distribution of the change features, a tree-structured dictionary is learned from the pseudotraining set and the unlabeled data. The sparse reconstruction error, a more robust distance compared to the Euclidean distance, is used to determine the label of each change feature. Comparative experiments demonstrate the effectiveness of the proposed method.

Detecting humans using luminance saliency in thermal images

This Letter introduces an efficient human detection method in thermal images, using a center-symmetric local binary pattern (CS-LBP) with a luminance saliency map and a random forest (RF) classifier scheme. After detecting a candidate human region, we crop only the head and shoulder region, which has a higher thermal spectrum than the legs or trunk. The CS-LBP feature is then extracted from the luminance saliency map of a hotspot and applied to the RF classifier, which is an ensemble of randomized decision trees. We demonstrate that our detection method is more robust than conventional feature descriptors and classifiers in thermal images.