Fatigue Expression Recognition Algorithm Based on Reconstructed LBP-HOG (LBP-RHOG) Feature
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Abstract This paper aims at two problems in fatigue expression recognition. First, texture features extracted by LBP (Local Binary Patterns) descriptor are limited and can not effectively describe the edge and direction information of image. Second, structural features extracted by HOG (Histogram of Oriented Gradient) descriptor are redundant and its computational complexity is high. To fill the gaps of these two problems, we proposed a reconstructed LBPHOG (LBP-RHOG) algorithm which extracted texture spectrum features and edge features from LBP operator and reconstructed HOG operator respectively and obtain fusion information by fusing these two features. To better evaluate the recognition performance, we complete simulation under a self-built fatigue expression database. The results show that our method has low computational complexity and high recognition rate, and can identify fatigue state well.Keywords:
Local Binary Patterns
Feature (linguistics)
Expression (computer science)
Recognizing human action from video sequences has lots of applications that make it an interesting research subject. Motion History Image (MHI) is a good spatio-temporal template to represent the distinctive profile of an action using a single image. However, in this paper, we use Local Binary Patterns (LBP) to extract the highlighted features from the spatio-temporal template and formulate them as a histogram to make the feature vector. Rather than MHL we use Directional MHI (DMHI) for this purpose. We also use shape feature taken from selective silhouettes and concatenate them with LBP histograms. We measured the performance of the proposed action representation method along with some variants of it by employing Weizmann action dataset and found reasonably higher accuracy for practical use.
Local Binary Patterns
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Action Recognition
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In order to improve the efficiency of traffic management in urban areas, a recognition method of abnormal vehicle behaviors in urban traffic scenes using combined features of histograms of oriented gradient (HOG) and local binary pattern (LBP) is proposed in this paper. Firstly, three feature extraction methods, such as histograms of oriented gradient, local binary pattern (LBP), and edge orientation histogram (EOH) are analyzed, comparatively. Secondly, the experiments of abnormal vehicle behaviors, such as normal driving, running a red light, pressing line, and illegal vehicle steering, are conducted using combined features and support vector machines. Results showed that the combined features of HOG and LBP offers the best classification performance, and the recognition rate is over 93.6%. With combined features of HOG and LBP, the classification accuracies of illegal vehicle steering are over 82.7% in the experiments.
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In this paper we propose a dedicated hardware for extraction of local binary pattern (LBP) feature vectors. The LBP method transforms local features of image data into binary micro-patterns that represent local and global features of the image. The LBP method can be used in applications such as texture classification, moving object detection and face detection and recognition. The hardware proposed in this paper has a massively parallel architecture in order to speed up the LBP feature extraction in real-time applications. The image data is pre-processed using an analog comparison method. Therefore, simulations are performed to find out how mismatch affects the performance.
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This paper addresses the challenge of recognizing dynamic textures based on spatial-temporal descriptors. Dynamic textures are composed of both spatial and temporal features. The histogram of local binary pattern (LBP) has been used in dynamic texture recognition. However, its performance is limited by the reliability issues of the LBP histograms. In this paper, two learning-based approaches are proposed to remove the unreliable information in LBP features by utilizing Principal Histogram Analysis. Furthermore, a super histogram is proposed to improve the reliability of the LBP histograms. The temporal information is partially transferred to the super histogram. The proposed approaches are evaluated on two widely used benchmark databases: UCLA and Dyntex++ databases. Superior performance is demonstrated compared with the state of the arts.
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In this paper, a new face recognition method based on local binary pattern(LBP) histogram is presented. First, the grey face image is divided into several parts, and then the LBP histogram of each part is calculated and concatenated together. Second, extract other concatenated histograms using different LBP operators and image blocking patterns. All the histograms above are concatenated into the ultimate vector. The proposed approach was tested using Yale face database on Android platform. Experimental results demonstrate that the combined LBP histogram feature has an encouraging performance for face recognition.
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In this paper, we present a noise tolerant descriptor based on a local binary pattern (LBP) method. Due to threshold-based operations, these types of LBP methods are sensitive to noise factors. The use of a robust LBP (RLBP) reduced some noise effects. However, it may lead to a loss of subtle local texture information. Instead of concatenating the LBP and RLBP features, we produced a histogram as a weighted sum of the histograms of the LBPs and the RLBP. The proposed noise tolerant LBP (NTLBP) was calculated using the LBP histogram and histogram voting results of the RLBP. Without increasing the number of features, NTLBP proved to be robust against noise effects. We conducted several gender classification experiments using the FERET database and the NTLBP outperformed both the LBP and the RLBP methods.
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The traditional LBP Histogram representation extracts the local micro-patterns and assigns the same weight all local micro-patterns. To combine the different contribution to face recognition, this paper proposes a weighted LBP histogram based on Weber's law. Firstly, inspired by psychological Weber's law, intensity of local micro-pattern is defined by the ratio between two terms: one is relative intensity differences of a central pixel against its neighbors, the other is intensity of local central pixel. Secondly, regarding the intensity of local micro-pattern as its weight, the weighted LBP histogram is constructed with the defined weight. Finally, to make full use of the space location information and lessen the complexity of recognition, the partitioning and uniform patterns are applied to get final features. The experiment results demonstrate that the proposed method outperforms the methods based on traditional LBP.
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This paper proposes an efficient method for increasing the performance of Local Binary Patterns (LBPs). Although histogram of LBPs provides sufficient information of local pattern occurrences, it discards global interaction of these local patterns. We replace histogram of LBPs by making a Local Pattern Co-occurrence Matrix (LPCM) for the purpose of rotation and illumination invariant texture classification. Experimental results show significant improvement in terms of classification accuracy in comparison with conventional histogram based feature extraction method.
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In recent years, local pattern based features have attracted increasing interest in object detection and recognition systems. Local Binary Pattern (LBP) feature is widely used in texture classification and face detection. But the original definition of LBP is not suitable for human detection. In this paper, we propose a novel feature named gradient local binary patterns (GLBP) for human detection. In this feature, original 256 local binary patterns are reduced to 56 patterns. These 56 patterns named uniform patterns are used for generating a 56-bin histogram. And gradient value of each pixel is set as the weight which is always same in LBP based features in histogram calculation to computing the values in 56 bins for histogram. Experiments are performed on INRIA dataset, which shows the proposal GLBP feature is discriminative than histogram of orientated gradient (HOG), Semantic Local Binary Patterns (S-LBP) and histogram of template (HOT). In our experiments, the window size is fixed. That means the performance can be improved by boosting methods. And the computation of GLBP feature is parallel, which make it easy for hardware acceleration. These factors make GLBP feature possible for real-time pedestrian detection.
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Pedestrian detection
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The traditional local binary pattern (LBP) histogram representation extracts the local micropatterns and assigns the same weight to all local micropatterns. To combine the different contributions of local micropatterns to face recognition, this paper proposes a weighted LBP histogram based on Weber's law. First, inspired by psychological Weber's law, intensity of local micropattern is defined by the ratio between two terms: one is relative intensity differences of a central pixel against its neighbors and the other is intensity of local central pixel. Second, regarding the intensity of local micropattern as its weight, the weighted LBP histogram is constructed with the defined weight. Finally, to make full use of the space location information and lessen the complexity of recognition, the partitioning and locality preserving projection are applied to get final features. The proposed method is tested on our infrared face databases and yields the recognition rate of 99.2% for same-session situation and 96.4% for elapsed-time situation compared to the 97.6 and 92.1% produced by the method based on traditional LBP.
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