Face Description with Local Binary Patterns and Local Ternary Patterns: Improving Face Recognition Performance Using Similarity Feature-Based Selection and Classification Algorithm

Tran, Chi-Kien; Lee, Tsair-Fwu; Chang, Liyun; Chao, Pei Ju

doi:10.1109/is3c.2014.141

Cited by 16 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to compare the efficiency of proposed and conventional methods, LBP [11] descriptor was used to represent the face image and histogram-based feature was extracted from obtained images. The chi-square distance [6], [11], [12] was chosen for nearest neighbor classifier. The conventional method (CM) is a face recognition method using nearest neighbor classifier.…”

Section: B Experimental Settingsmentioning

confidence: 99%

“…The goal of the algorithms is to retain similarity features of the training images in a class in order to minimize within-class differences, while maximizing between-class differences and to use this feature set for classification. They have been proven an efficient tool for improving the performance of face recognition systems using local binary patterns (LBP), local ternary patterns, and local directional pattern (LDP) features [6], [7]. However, SFSC algorithms still have a limitation as the value of threshold parameter is not automatically set, meaning that user needs to test many different values of threshold to find the best similarity feature set.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Face Recognition Based on similarity Feature-Based Selection and Classification Algorithms and Wrapper Model

Tran¹

2019

IJMLC

View full text Add to dashboard Cite

Similarity feature-based selection and classification (SFSC) algorithms, introduced by Tran et al. in 2013, have been used as a tool to reduce storage cost and increase performance of face recognition systems. However, these still exist a problem when automatically selecting a suitable threshold. This paper introduces a new approach, which combines SFSC algorithms, and a wrapper model, to automatically select a suitable threshold and improve face recognition accuracy. The training face image set (which is split into two separated subsets including a training subset and a wrapper subset) is utilized as data input for the similarity feature-based selection algorithm in combination with the wrapper model to identify a best feature set. The obtained feature set will be used for classification. The experiments were conducted on the histogram-based feature and two databases, ORL database of faces and Georgia Tech face database. The results demonstrated that the proposed algorithm not only allowed for automatic detection of the suitable feature set, but also achieved a better recognition accuracy compared to conventional algorithms.

show abstract

Section: B Experimental Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Face Recognition Based on similarity Feature-Based Selection and Classification Algorithms and Wrapper Model

Tran¹

2019

IJMLC

View full text Add to dashboard Cite

show abstract

“…Due to its relative simplicity, LBP has been applied successfully in many applications. The algorithm uses 3 × 3 windows of neighborhood pixels in the image to determine the new value of a pixel being considered (Ahonen et al, 2006;Ojala et al, 2010, Tran et al, 2014. Consider Figure 1, initially, the algorithms probes the 8-neighbood pixels around pixel .…”

Section: Local Binary Patternmentioning

confidence: 99%

A Monogenic Local Gabor Binary Pattern for Facial Expression Recognition

Eleyan

Ashir

2017

Scitech

View full text Add to dashboard Cite

ABSTRACT:The paper implements a monogenic-Local Binary Pattern (mono-LBP) algorithm on Local Gabor Pattern (LGP). The proposed approach initially features from the samples using LGP at different scales and orientation. The extracted LGP features are further enhanced by decomposing it into three monogenic LBP channels before being recombined to generate the final feature vector. Different Normalization schemes are applied to the final feature vector. Two best performing normalization algorithms with mono-LBP are fused at score level to obtain an improved performance using K-Nearest Neighbor classifier with L1-norm as a distance metrics. Moreover, performance comparison is done with other variants of LGP algorithm and also the effects of various normalization techniques are investigated. Experimental results from JAFFE and TFEID facial expression databases show that the new technique has improved performance compared to its counterparts. Key Words: Facial expression recognition, Local gabor patterns, Monogenic local binary patterns. Yüz İfade Tanınması İçin Bir Monojenik Yerel Gabor İkili DesenÖZ: Bu makale, yerel Gabor Desen (LGP) üzerinde monojenik-Yerel İkili Desen (mono-LBP) algoritmasını uygular. Önerilen algoritma Gabor çekirdeğinin farklı ölçeklerinde ve farklı normalizasyon şemaları ile uygulanır. Mono-LBP ile en iyi performans gösteren normalleştirme algoritmalarından elde edilen sonuçlar, geliştirilmiş bir performans elde etmek için skor düzeyinde birleştirilmiştir. Üstelik, performans karşılaştırması diğer LGP algoritmasının türevleri ile yapılmıştır ve ayrıca çeşitli normalleştirme tekniklerinin etkileri araştırılmaktadır. JAFFE yüz ifadesi veritabanında yapılan deneysel sonuçlarine göre, önerilen yaklaşım bir sınıflandırıcı olarak mesafe metrikini kullanarak mevcut algoritmalara kıyasla en iyi ortalama performansa sahip olduğunu göstermektedir. Anahtar Kelimeler: Yüz ifade tanıma, Yerel gabor desen, Monojenik yerel ikili desen.

show abstract

“…The LBP code can explain the data using the differences between a sample and its neighbours [15,16]. LBPs have been widely used, particularly in face recognition systems [16][17][18]. At a fixed pixel position, the LBP operator is described as an ordered set of binary comparisons of pixel intensities between the centre pixel and its neighbouring pixels.…”

Section: D Local Binary Patternmentioning

confidence: 99%

A New Feature Extraction Technique Based on 1D Local Binary Pattern for Gear Fault Detection

Abdul¹,

Al-Talabani

Abdulrahman

2016

Shock and Vibration

View full text Add to dashboard Cite

Gear fault detection is one of the underlying research areas in the field of condition monitoring of rotating machines. Many methods have been proposed as an approach. One of the major tasks to obtain the best fault detection is to examine what type of feature(s) should be taken out to clarify/improve the situation. In this paper, a new method is used to extract features from the vibration signal, called 1D local binary pattern (1D LBP). Vibration signals of a rotating machine with normal, break, and crack gears are processed for feature extraction. The extracted features from the original signals are utilized as inputs to a classifier based onk-Nearest Neighbour (k-NN) and Support Vector Machine (SVM) for three classes (normal, break, or crack). The effectiveness of the proposed approach is evaluated for gear fault detection, on the vibration data obtained from the Prognostic Health Monitoring (PHM’09) Data Challenge. The experiment results show that the 1D LBP method can extract the effective and relevant features for detecting fault in the gear. Moreover, we have adopted the LOSO and LOLO cross-validation approaches to investigate the effects of speed and load in fault detection.

show abstract

Face Description with Local Binary Patterns and Local Ternary Patterns: Improving Face Recognition Performance Using Similarity Feature-Based Selection and Classification Algorithm

Cited by 16 publications

References 15 publications

Face Recognition Based on similarity Feature-Based Selection and Classification Algorithms and Wrapper Model

Face Recognition Based on similarity Feature-Based Selection and Classification Algorithms and Wrapper Model

A Monogenic Local Gabor Binary Pattern for Facial Expression Recognition

A New Feature Extraction Technique Based on 1D Local Binary Pattern for Gear Fault Detection

Contact Info

Product

Resources

About