Using novel shape, color and texture descriptors for human hand detection
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In this paper, we present a robust feature set to detect human hands in still images having simple as well as complex backgrounds. Our method relies on using a blend of existing and new shape-based, color-based and texture-based features. First, we identify the shortcomings of two existing features: Histograms of Oriented Gradient (HOG) and Color Name (CN). For HOG, we investigate the scenarios where the traditional block normalization schemes generate noisy results in near uniform regions in the image background and impede the accurate detection of human hands. We offer a more effective block normalization scheme for our new shape-based feature, αHOG, which results in considerably improved detection. Our new color-based feature, Clipped Color Name (CCN), caters for the noise induced color labels encountered in the CN feature, by modifying the probability assignment method for the basic colors in each pixel. For capturing the texture cues, we employ Local Binary Patterns (LBP) and Local Trinary Patterns (LTP). We compare the relative performance of the individual features in isolation and in different feature sets. For feature sets' comparison, the issue of high dimensional feature space generated as a result of feature fusion is addressed by using Partial Least Squares (PLS) for dimensionality reduction. Subsequently, we employ the non-linear Radial Basis Function Support Vector Machine (RBF SVM) classifier on PLS reduced feature sets. In our experiments, we use two different image datasets, namely the benchmark Cambridge Gesture Dataset (having simple backgrounds) and our own dataset (having a wider variety of complex backgrounds). Based on the experimental results, we find that out of the four feature sets we use, the feature set consisting of αHOG, CCN and LTP gives the best results in terms of the combined criteria of classification accuracy and computation time, and also offers improvement over the feature set proposed by Hussain and Triggs [1].Keywords:
Local Binary Patterns
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In this paper, unique features of the segmented image samples are extracted by using two major feature extraction techniques: Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG). After this, these features are fused to get more precise and productive outcomes. The average accuracy of the three distinct datasets that were generated using the LBP and HOG features are determined. To calculate the accuracy of the three distinct models, classification techniques like KNN and SVM, are adopted.
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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.
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Local Binary Patterns
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Action Recognition
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Face recognition is a process of verifying an individual using facial images and it is widely employed in identifying people on social media platforms, validating identity at ATMs, finding missing persons, controlling access to sensitive areas, finding lost pets, etc. Face recognition is still a trending research area because of various challenges like illumination variations, different poses, and expressions of the person. Here, a novel methodology is introduced for face recognition using Histogram of Oriented Gradients (HOG), histogram of Local Binary Patterns (LBP), and Convolutional Neural Network (CNN). The features from HOG, histogram of LBP, and deep features from the proposed CNN are linearly concatenated to produce the feature space and then classified by Support Vector Machine. The face databases ORL, Extended Yale B, and CMUPIE are used for experimental work and attained a recognition rate of 98.48%, 97.33%, and 97.28% respectively.
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Three-dimensional face recognition
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Histograms of Local Binary Patterns (LBPs) and variations thereof are a popular local visual descriptor for face recognition. So far, most variations of LBP are designed by hand or are learned with non-supervised methods. In this work we propose a simple method to learn discriminative LBPs in a supervised manner. The method represents an LBP-like descriptor as a set of pixel comparisons within a neighborhood and heuristically seeks for a set of pixel comparisons so as to maximize a Fisher separability criterion for the resulting histograms. Tests on standard face recognition datasets show that this method can create compact yet discriminative descriptors.
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fMPE is a previously introduced form of discriminative training, in which offsets to the features are obtained by training a projection from a high-dimensional feature space based on posteriors of Gaussians. This paper presents recent improvements to fMPE, including improved high-dimensional features which are easier to compute, and improvements to the training procedure. Other issues investigated include cross-testing of fMPE transforms (i.e. using acoustic models other than those with which the fMPE was trained) and the best way to train the Gaussians used to obtain the vector of posteriors.
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This paper examines a novel binary feature referred to as the Local Hybrid Patterns (LHP) that is generated by mixing highly discriminative bits of the binary local pattern features (BLPFs) such as the Local Binary Patterns (LBP), Local Gradient Patterns (LGP), and Mean LBP (MLBP). Starting with the most discriminative BLPF selected, the LHP generating algorithm iteratively updates the bits of the selected BLPF by replacing the least discriminative bit with the most discriminative bit of all the candidate BLPFs. At the expense of a small increase in computation, the LHP is guaranteed to give smaller or equal empirical error compared to any BLPFs considered in the pool. Experimental comparison of different sets of features consistently shows that the LHP leads to better performance than previously proposed methods under the AdaBoost face detection framework on MIT+CMU and FDDB benchmark datasets.
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Local Binary Patterns (LBP) have been well exploited for facial image analysis recently. In the existing work, the LBP histograms are extracted from local facial regions, and used as a whole for the regional description. However, not all bins in the LBP histogram are necessary to be useful for facial representation. In this paper, we propose to learn discriminative LBP-Histogram (LBPH) bins for the task of facial expression recognition. Our experiments illustrate that the selected LBPH bins provide a compact and discriminative facial representation. We experimentally illustrate that it is necessary to consider multiscale LBP for representing faces, and most discriminative information is contained in uniform patterns. By adopting SVM with the selected multiscale LBPH bins, we obtain the best recognition performance of 93.1% on the Cohn-Kanade database.
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A robust image processing technique capable of detecting and localizing objects accurately plays an important role in many computer vision applications. In this paper, a feature based detector for birds is proposed. By combining Histogram of Oriented Gradients (HOG) and Center-Symmetric Local Binary Pattern (CS-LBP) as the feature set, detection of crows under various lighting conditions could be carried out. A dataset of crow birds with a wide range of poses and backgrounds was prepared and learned using linear Support Vector Machine (SVM). Experiments on different test images show that HOG and CS-LBP based descriptors can achieve 87% accuracy.
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