Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

Search 250 million research papers

Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.

About 3,851 papers for “Local binary patterns”

Circle-Block Local Binary Patterns for Face Recognition

Yifan Tang, Cairong Zhao, Duoqian Miao · 2015

In this paper a new texture operator based on Local Binary Patterns (LBP) was proposed for face recognition called Circle-Block Local Binary Patterns (CB-LBP). The mean gray values of a circle block pixels are calculated to achieve macroscopic feature extraction. Our method was compared with the LBP-based patterns tested on Extended Yale B face image databases. Experimental results show the effectiveness of the proposed method.

Cited by 0Open access

Crowd Density Estimation Based on Local Binary Patterns

Antonio Kuzminski · FER Repository · 2020

Procjena gustoće mnoštva od velike je važnosti u video nadzoru i upravljanju mnoštvom. Predložene su različite metode za rješavanje ovog problema, međutim, većina ih procjenjuje gustoću mnoštva cijele slike pritom ignorirajući lokalna mnoštva. U ovom radu primjenjuje se operator lokalnih binarnih značajki nad sivim slikama i sivim slikama nad kojima je primjenjen operator gradijenta. Iz takvih slika se pomoću matrice pojavnosti lokalnih binarnih značajki, postupkom klizećeg okna, izračunavaju Haralickove značajke koje tvore vektore značajki. Koriste se dvije vrste klasifikatora: k-NN i SVM koji se na kraju spajaju u jedan klasifikator i utjecaj svakog klasifikatora uvjetovan je njegovom točnošću klasifikacije.

Cited by 0

Local Binary Patterns for Dorsal Hand Vein Recognition

Jayanti Yusmah Sari, Suharsono Bantun, Z Noorhasanah et al. · 2021

Biometric system is a more promising identification method than using cards and passwords. One of the physiological biometrics that is currently being developed is the dorsal hand vein pattern. The pattern of vein contains information that is able to authenticate individual identities for the purpose of personal authentication automatically. Vein patterns can be recognized by using a feature extraction approach. Therefore, in this study the Local Binary Patterns (LBP) method was used. LBP is used as a feature extraction method to optimize the texture feature of vein to obtain optimal accuracy and processing speed. The implementation of Local Binary Patterns (LBP) method for the dorsal hand vein recognition system was tested on 200 datasets. The test results show recognition accuracy up to 94% with an average accuracy of 90.67%.

Cited by 3

Pedestrian detection using center-symmetric local binary patterns

Yongbin Zheng, Chunhua Shen, Richard Hartley et al. · 2010

Abstract—Accurately detecting pedestrians in images plays a critically important role in many computer vision applications. Extraction of effective features is the key to this task. Promising features should be discriminative, robust to various variations and easy to compute. In this work, we present novel features, termed dense center-symmetric local binary patterns (CS-LBP) and pyramid center-symmetric local binary/ternary patterns (CS-LBP/LTP), for pedestrian detection. The standard LBP proposed by Ojala et al. [1] mainly captures the texture infor-mation. The proposed CS-LBP feature, in contrast, captures the gradient information and some texture information. Moreover, the proposed dense CS-LBP and the pyramid CS-LBP/LTP are easy to implement and computationally efficient, which is desirable for real-time applications. Experiments on the INRIA pedestrian dataset show that the dense CS-LBP feature with linear supporct vector machines (SVMs) is comparable with the histograms of oriented gradients (HOG) feature with linear SVMs, and the pyramid CS-LBP/LTP features outperform both HOG features with linear SVMs and the start-of-the-art pyramid HOG (PHOG) feature with the histogram intersection kernel SVMs. We also demonstrate that the combination of our pyramid CS-LBP feature and the PHOG feature could significantly improve the detection performance—producing state-of-the-art accuracy on the INRIA pedestrian dataset. Index Terms—Pedestrian detection, Dense center-symmetric local binary patterns, Pyramid center-symmetric local binary-/trinary patterns. I.

Cited by 0

Improving background subtraction using Local Binary Similarity Patterns

Pierre-Luc St-Charles, Guillaume-Alexandre Bilodeau · IEEE Winter Conference on Applications of Computer Vision · 2014

Most of the recently published background subtraction methods can still be classified as pixel-based, as most of their analysis is still only done using pixel-by-pixel comparisons. Few others might be regarded as spatial-based (or even spatiotemporal-based) methods, as they take into account the neighborhood of each analyzed pixel. Although the latter types can be viewed as improvements in many cases, most of the methods that have been proposed so far suffer in complexity, processing speed, and/or versatility when compared to their simpler pixel-based counterparts. In this paper, we present an adaptive background subtraction method, derived from the low-cost and highly efficient ViBe method, which uses a spatiotemporal binary similarity descriptor instead of simply relying on pixel intensities as its core component. We then test this method on multiple video sequences and show that by only replacing the core component of a pixel-based method it is possible to dramatically improve its overall performance while keeping memory usage, complexity and speed at acceptable levels for online applications.

Cited by 156

Local binary patterns based on α-cutting approach

Marija Delić · Journal of Electronic Imaging · 2020

Local binary patterns (LBP) are well documented in the literature as descriptors of local image texture, and their histograms have been shown to be well-performing texture features. A method for texture description that is based on the α-cutting approach is presented. The presented approach combines basic definitions from the fuzzy set theory with the main concept of LBP descriptors, which resulted in powerful texture features. The general method is introduced and defined and its binary, ternary, and quinary versions evaluated in tests produced excellent results in texture classification. The performance of our method is presented by an extensive evaluation on four datasets—KTH-TIPS2b, UIUC, Virus, and Brodatz. The introduced descriptors are compared with some of the classical approaches—LBP, improved LBP, local ternary pattern, including one very promising LBP variant—median robust extended LBP (MRELBP), as well as with three non-LBP methods, based on deep convolutional neural networks approaches—ScatNet, FV-AlexNet, and fisher vector based very deep VGG. Our method effectively deals with many classification challenges and exceeds most of the other approaches. It outperforms the classical approaches on all datasets, even in its simplest binary version. It outperforms the MRELBP descriptor on the UIUC, KTH-TIPS2b, and Brodatz datasets and reaches a better classification performance than two out of the three deep learning approaches on the KTH-TIPS2b dataset.

Cited by 1

Face recognition using statistical adapted local binary patterns.

Abdallah A. Mohamed · 2013

Biometrics is the study of methods of recognizing humans based on their behavioral and physical characteristics or traits. Face recognition is one of the biometric modalities that received a great amount of attention from many researchers during the past few decades because of its potential applications in a variety of security domains. Face recognition however is not only concerned with recognizing human faces, but also with recognizing faces of non-biological entities or avatars. Fortunately, the need for secure and affordable virtual worlds is attracting the attention of many researchers who seek to find fast, automatic and reliable ways to identify virtual worlds’ avatars. In this work, I propose new techniques for recognizing avatar faces, which also can be applied to recognize human faces. Proposed methods are based mainly on a well-known and efficient local texture descriptor, Local Binary Pattern (LBP). I am applying different versions of LBP such as: Hierarchical Multi-scale Local Binary Patterns and Adaptive Local Binary Pattern with Directional Statistical Features in the wavelet space and discuss the effect of this application on the performance of each LBP version. In addition, I use a new version of LBP called Local Difference Pattern (LDP) with other well-known descriptors and classifiers to differentiate between human and avatar face images. The original LBP achieves high recognition rate if the tested images are pure but its performance gets worse if these images are corrupted by noise. To deal with this problem I propose a new definition to the original LBP in which the LBP descriptor will not threshold all the neighborhood pixel based on the central pixel value. A weight for each pixel in the neighborhood will be computed, a new value for each pixel will be calculated and then using simple statistical operations will be used to compute the new threshold, which will change automatically, based on the pixel’s values. This threshold can be applied with the original LBP or any other version of LBP and can be extended to work with Local Ternary Pattern (LTP) or any version of LTP to produce different versions of LTP for recognizing noisy avatar and human faces images.

Cited by 0Open access

A TensorFlow implementation of Local Binary Patterns Transform

Devrim Akgün · MANAS Journal of Engineering · 2021

Feature extraction layers like Local Binary Patterns (LBP) transform can be very useful for improving the accuracy of machine learning and deep learning models depending on the problem type. Direct implementations of such layers in Python may result in long running times, and training a computer vision model may be delayed significantly. For this purpose, TensorFlow framework enables developing accelerated custom operations based on the existing operations which already have support for accelerated hardware such as multicore CPU and GPU. In this study, LBP transform which is used for feature extraction in various applications, was implemented based on TensorFlow operations. The evaluations were done using both standard Python operations and TensorFlow library for performance comparisons. The experiments were realized using images in various dimensions and various batch sizes. Numerical results show that algorithm based on TensorFlow operations provides good acceleration rates over Python runs. The implementation of LBP can be used for the accelerated computing for various feature extraction purposes including machine learning as well as in deep learning applications.

Cited by 3Open access

Face Detection and Recognition using Local Binary Patterns

Drakshayani Desai, Ramakrishna Seemakurti · 2017

The human face conveys a lot of information about identity and emotional state of a person. Face recognition is an interesting and challenging task and impacts important applications in many areas such as identification for law enforcement, authentication for banking and security system access and personal identification. This paper proposes an Automatic Face Verification system using Local Binary Patterns (LBP). It is a texture based algorithm used for face recognition that describes the texture and shape of digital face image. The digital facial image is first divided into small blocks from which Local Binary Patterns are formed and then concatenated into a single feature vector. This feature vector plays an important role in efficient representation of the face and is used to measure similarities by calculating the distance between feature points.

Cited by 2

Facial expression recognition using enhanced local binary patterns

Augustine Nnamdi Ekweariri, Kamil Yurtkan · 2017

Facial expression, a non-verbal communication, is a means through which humans convey their inner emotional state, thus playing an important role in social interaction and interpersonal relations. Facial expression recognition plays a significant role in human-computer interaction as well as various fields of behavioral science. There are six known classes of emotional state which are anger, disgust, fear, happiness, sadness and surprise, associated with their respective facial expressions, according to Ekman's studies. Humans recognize facial expressions almost effortlessly and without delay, but this is quite challenging for digital computers. The paper presents facial expression recognition using local binary patterns. The main contribution of the paper is the feature selection applied, in which the high variance LBP pixels are selected to represent faces. By selecting the high variance pixels based on LBPs, the recognition rates were improved significantly. The tests are completed on the BU-3DFE database. The experiments show that after applying feature selection, the recognition rates are improved by 11%.

Cited by 28

Description of Interest Regions with Local Binary Patterns

Marko Heikkilä A, Matti Pietikäinen A, Cordelia Schmid B · 2013

This paper presents a novel method for interest region description. We adopted the idea that the appearance of an interest region can be well characterized by the distribution of its local features. The most well-known descriptor built on this idea is the SIFT descriptor that uses gradient as the local feature. Thus far, existing texture features are not widely utilized in the context of region description. In this paper, we introduce a new texture feature called center-symmetric local binary pattern (CS-LBP) that is a modified version of the well-known local binary pattern (LBP) feature. To combine the strengths of the SIFT and LBP, we use the CS-LBP as the local feature in the SIFT algorithm. The resulting descriptor is called the CS-LBP descriptor. In the matching and object category classification experiments, our descriptor performs favorably compared to the SIFT. Furthermore, the CS-LBP descriptor is computationally simpler than the SIFT. Key words: Region description, region detection, local binary patterns, SIFT, image matching,

Cited by 0

Face anti-spoofing via deep local binary patterns

Lei Li, Xiaoyi Feng, Zetao Jiang et al. · 2017

Convolutional neural networks (CNNs) have achieved excellent performance in the field of pattern recognition when huge amount of training data is available. However, training a CNN model is less obvious when only a limited amount of data is given such as in the case of face anti-spoofing problem. It is indeed not easy to collect very large sets of fake faces. Especially for the fully-connected layers, tens of thousands of parameters need to be learned. To tackle this problem of lack of training data in face anti-spoofing, we propose to explore the incorporation of hand-crafted features in the CNN framework. In our proposed approach, the color local binary patterns (LBP) features are extracted from the convolutional feature maps, which are fine tuned based on the VGG-face model. These features are then fed into support vector machine (SVM) classifier. Extensive experiments are conducted on two benchmark and publicly available databases showing very interesting performance compared to state-of-the-art methods.

Cited by 33

Local Binary Patterns Histograms (LBPH) Based Face Recognition

Satya Bhushan Verma, Er. Nidhi Tiwari · International Journal of Engineering and Advanced Technology · 2019

The human face has been broadly used in computer vision field for individual recognition. The face recognition is one of the secure ways to protect the data over the internet. In this paper we use (LBPH) Local Binary Patterns Histogram based Face Recognition. We use Yale face database for experiment and it contains 165 grey images in the GIF format of 15 person and 11 image per person and in this experiment we use only normal image in 180*180 at grey scale images and in this research article in the verification phase the difference between two histograms are calculated by Chi-square distance, Manhattan distance. The proposed technique has achieved TSR=98.8% in Chi-square and TSR=98.5% in Manhattan distance parameter. Person Identification using their physical structure or behavioral characteristic is known as the biometric.

Cited by 1Open access