Local binary pattern and its derivatives for face recognition

This paper proposes a novel high-order local pattern descriptor, local derivative pattern (LDP), for face recognition. LDP is a general framework to encode directional pattern features based on local derivative variations. The n(th)-order LDP is proposed to encode the (n-1)(th) -order local derivative direction variations, which can capture more detailed information than the first-order local pattern used in local binary pattern (LBP). Different from LBP encoding the relationship between the central point and its neighbors, the LDP templates extract high-order local information by encoding various distinctive spatial relationships contained in a given local region. Both gray-level images and Gabor feature images are used to evaluate the comparative performances of LDP and LBP. Extensive experimental results on FERET, CAS-PEAL, CMU-PIE, Extended Yale B, and FRGC databases show that the high-order LDP consistently performs much better than LBP for both face identification and face verification under various conditions.

In this paper, a novel local pattern descriptor generated by the proposed local vector pattern (LVP) in high-order derivative space is presented for face recognition. The proposed vector representation of the referenced pixel is generated to provide the one-dimensional structure of micropatterns. To effectively extract more detailed discriminative information in a given sub-region, the vector of LVP is refined by varying local derivative directions from the nth-order LVP in (n−1)th-order derivative space. The proposed LVP is compared with the existing local pattern descriptors including local binary pattern (LBP), local derivative pattern (LDP), and local tetra pattern (LTrP) to evaluate the performances from input grayscale face images. Extensive experiments conducting on benchmark face image databases, FERET and Extended Yale B, demonstrate that the proposed LVP in high-order derivative space indeed performs much better than LBP, LDP and LTrP for face recognition.

Content based image retrieval is grievous need of present scenario in digital imaging world. This work presents a new multi-scale content based image retrieval system which leverages the multi-resolution property of discrete wavelet transform (DWT) and the local information attribute of local extrema patterns (LEPs). Two level DWT is applied on images and wavelet coefficients are obtained for images, further the LEPs are collected from wavelet coefficients to extract the feature vector. The proposed method abbreviated as DWT+LEP and tested on two benchmark databases for validation and compared with local extrema patterns (LEPs), discrete wavelet transform (DWT), center-symmetric local binary pattern (CS LBP), local edge pattern for image retrieval (LEPINV), local edge pattern for segmentation (LEPSEG), local binary pattern (LBP) and block based local binary pattern (BLK LBP).

Histogram refinement for texture descriptor based image retrieval

The Local Binary Pattern (LBP) and its variants are capable of extracting image texture and have been successfully applied to classification. However, LBP has not been used to extract and describe the texture of polarized images, and simple LBP cannot characterize the polarized texture information from different polarizations of angles. In order to solve these problems, we propose a new multi-angle orthogonal difference polarization image texture descriptor (MODP_ITD) by analyzing the relationship between the difference of orthogonal difference polarization images from different angles and the pixel intensity distribution in the local neighborhood of images from different angles. The MODP_ITD consists of three patterns: multi-angle polarization orthogonal difference local binary pattern (MODP_LBP), multi-angle polarization orthogonal difference local sampling point principal component sequence pattern (MODP_LPCSP) and multi-angle orthogonal difference polarization local difference binary pattern (MODP_LDBP). The MODP_LBP extracts local corresponding texture characteristics of polarized orthogonal difference images from multiple angles. The MODP_LPCSP sorts the principal component order of each angle orthogonal difference local sampling point. The MODP LDBP extracts the local difference characteristics between different angles by constructing a new polarized image. Then, the frequency histograms of MODP_LBP, MOD_LPCSP ,and MODP_LDBP are cascaded to generate MODP_ITD, so as to distinguish local neighborhoods. By using vertical and parallel polarization and unpolarized light active illumination, combined with the measurements at three different detection zenith angles, we constructed a polarization texture image database. A substantial number of experimental results on the self-built database show that our proposed MODP_ITD can represent the detailed information of polarization images texture. In addition, compared with the existing LBP methods, The MODP_ITD has a competitive advantage in classification accuracy.

This paper presents an analysis of the statistical texture representation of the Local Binary Pattern (LBP) variants in the classification of wood defect images. The basic and variants of the LBP feature set that was constructed from a stage of feature extraction processes with the Basic LBP, Rotation Invariant LBP, Uniform LBP, and Rotation Invariant Uniform LBP. For significantly discriminating, the wood defect classes were further evaluated with the use of different classifiers. By comparing the results of the classification performances that had been conducted across the multiple wood species, the Uniform LBP was found to have demonstrated the highest accuracy level in the classification of the wood defects.

Automated recognition of facial expression has attracted significant attention in recent years due to its potential applicability in security and surveillance, human computer interaction, social robotics, and animation. This paper presents a new facial expression recognition method that utilizes bit plane specific local image description in a weighted score level fusion. The motivation is to utilize bit plane slicing to highlight the contribution of a particular bit plane made to the holistic facial appearance, which is then used in a weighted score level fusion in order to boost the recognition performance. A new local image descriptor is proposed specifically to extract local features from bit plane representations that utilizes Fisher linear discriminant to maximize the inter-class distance, while minimizing the intra-class variance. Two well-known facial expression databases, namely the Cohn-Kanade (CK) and the Japanese female facial expression (JAFFE) database have been used to evaluate the performance of the proposed method against existing facial appearance descriptors, such as local binary pattern (LBP), local ternary pattern (LTP), local directional pattern (LDP), and linear discriminant analysis (LDA). Experiments with a total of seven prototypic facial expressions show promising results for the proposed method, as compared with the other existing methods.

Feature extraction on local jet space for texture classification

Automatic image annotation is the process of providing tags to salient objects in the image. The aim is achieved by first identifying salient objects. For this, the traditional gradient vector flow (GVF) model is modified to incorporate saliency by adding minimum directional contrast to the data part in the energy functional of GVF. To provide tags, three features: color, local binary pattern and local direction pattern are used. Classification is done by modifying cluster-based multi-label learning with feature-induced labeling information enrichment (C-MLFE) and is termed as C’-MLFE. This involves clustering the training data into two sets. For each cluster, a squared weight matrix records the influence of each instance on the other. This relationship among the training instance is used to enrich the labeling information of the test set. The result is compared with six state-of-the-art algorithms.

Humans interact socially with the help of facial expressions. Even health states or pains are reflected through facial expressions and hence can be useful in healthcare. Here, a facial expression recognition system is proposed. The bandlet transform is performed on face image to generate quadtree. Then on the output of bandlet transform centre symmetric - local binary pattern (CS-LBP) is applied. A feature vector of the image is generated by taking the histogram of CS-LBP. The support vector machine (SVM) is used to classify expressions in six categories. The experiments are performed using a publically available CK+ dataset. The initial results with LBP and CS-LBP are reported.

Palmprint is a reliable and unique biometric trait with high acceptability. In this paper, we propose a new Local Composition Derivative Pattern (LCDP) for palmprint recognition. LCDP extracts first order derivative information of images along radial and directional directions which can capture more detailed information than the non-directional local binary pattern (LBP). Different from LBP encoding just the binary result of the radial derivative among central pixel and local neighbors by using a simple threshold function with static threshold value, the LCDP extracts discriminative local information by composition of both radial and directional derivatives information by using a threshold function with dynamic threshold value which obtains by first-order derivative information among local neighbors. Experimental evaluation through palmprint recognition on the Hong Kong Polytechnic University 2D_3D_palmprint database demonstrates the LCDP performs much better than LBP for palmprint identification.

Effective foreground detection in dynamic scenes is a challenging task in computer vision applications. In this paper, we propose a novel background modeling method to tackle this problem. First, we propose a second-order center-symmetric local derivative pattern (CS-LDP) which extracts more detail information compared with the first-order center-symmetric local binary pattern (CS-LBP). Then by concatenating the CS-LBP and CS-LDP histograms, a new hybrid histogram feature is presented. The length of this histogram is much shorter than the local binary pattern (LBP) histogram. Based on this hybrid feature, a novel background modeling method is proposed where the pixel process is modeled with a group of adaptive hybrid histograms. The major advantage of our method is its low complexity. Experiments on three challenging sequences demonstrate that the proposed method is effective and fast, producing comparable results to state-of-art algorithm while reducing the computation time greatly.

High-resolution remote sensing images have rich texture information,and combined texture information and image spectral information can improve the recognition accuracy of surface feature.In this paper,a new extended Local Binary Patterns(LBP) texture was applied to the high-resolution images classification in comparison with classifications using spectral data only and using combined spectral data and LBP texture features.The results show that the extended LBP has a good anti-noise performance,and the classification of image including the extended LBP texture can achieve a higher accuracy than the classifications using spectral data alone and using combined spectral data and LBP texture features.

We investigated the feasibility of using multiresolution Local Binary Pattern (LBP) texture analysis to reduce falsepositive (FP) detection in a computerized mass detection framework. A new and novel approach for extracting LBP features is devised to differentiate masses and normal breast tissue on mammograms. In particular, to characterize the LBP texture patterns of the boundaries of masses, as well as to preserve the spatial structure pattern of the masses, two individual LBP texture patterns are then extracted from the core region and the ribbon region of pixels of the respective ROI regions, respectively. These two texture patterns are combined to produce the so-called multiresolution LBP feature of a given ROI. The proposed LBP texture analysis of the information in mass core region and its margin has clearly proven to be significant and is not sensitive to the precise location of the boundaries of masses. In this study, 89 mammograms were collected from the public MAIS database (DB). To perform a more realistic assessment of FP reduction process, the LBP texture analysis was applied directly to a total of 1,693 regions of interest (ROIs) automatically segmented by computer algorithm. Support Vector Machine (SVM) was applied for the classification of mass ROIs from ROIs containing normal tissue. Receiver Operating Characteristic (ROC) analysis was conducted to evaluate the classification accuracy and its improvement using multiresolution LBP features. With multiresolution LBP features, the classifier achieved an average area under the ROC curve, , z A of 0.956 during testing. In addition, the proposed LBP features outperform other state-of-the-arts features designed for false positive reduction.

Local binary pattern (LBP) is sensitive to noise. Local ternary pattern (LTP) partially solves this problem. Both LBP and LTP, however, treat the corrupted image patterns as they are. In view of this, we propose a noise-resistant LBP (NRLBP) to preserve the image local structures in presence of noise. The small pixel difference is vulnerable to noise. Thus, we encode it as an uncertain state first, and then determine its value based on the other bits of the LBP code. It is widely accepted that most of the image local structures are represented by uniform codes and noise patterns most likely fall into the non-uniform codes. Therefore, we assign the value of an uncertain bit hence as to form possible uniform codes. Thus, we develop an error-correction mechanism to recover the distorted image patterns. In addition, we find that some image patterns such as lines are not captured in uniform codes. Those line patterns may appear less frequently than uniform codes, but they represent a set of important local primitives for pattern recognition. Thus, we propose an extended noise-resistant LBP (ENRLBP) to capture line patterns. The proposed NRLBP and ENRLBP are more resistant to noise compared with LBP, LTP, and many other variants. On various applications, the proposed NRLBP and ENRLBP demonstrate superior performance to LBP/LTP variants.