Local binary patterns based on directional wavelet transform for expression and pose-invariant face recognition

Muqeet, Mohd. Abdul; Holambe, Raghunath S.

doi:10.1016/j.aci.2017.11.002

Cited by 40 publications

(40 citation statements)

References 20 publications

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“…In contrast to the nine directions [15] and five directions [16], we also used seven pre-assigned directions to implement 2-D IDW [17]. These directions are used to confirm a strong correlation among samples and to extract directional MRA features from face images.…”

Section: Implementation Of 2-d Improved Directional Wavelet (2-d Idw)mentioning

confidence: 99%

“…For numerous facial variations, substantial directional details can be estimated by approximating the edges [16,17] accountable for such variations which will considerably enhance the face identification performance which decides the basis of our method. The concept has been exploited in [15][16][17] for face recognition applications. This work extends the design of the adaptive directional scheme presented in [17] and presents an LBP-based IDW method to capture multi-resolution directional details from the face images.…”

Section: Introductionmentioning

confidence: 99%

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Face Identification Using LBP-Based Improved Directional Wavelet Transform

Muqeet¹,

Hameeduddin²

2021

Biometric Systems

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Face identification is the most active area of research in computer vision and biometric authentication. Various face identification methods are developed over the time, still, numerous facial appearances are needed to cope with such as facial expression, pose, and illumination variation. Moreover, faces captured in unrestrained situations also impose immense concern in designing effective face identification methods. It is desirable to extract robust local descriptive features to effectively characterize such facial variations both in unrestrained and restrained situations. This chapter discusses such a face identification method that incorporate a popular local descriptor such as local binary patterns (LBP) based on the improved directional wavelet transform (IDW) method to extract facial features. This designed method is applied to complex face databases such as CASIA-WebFace and LFW which consists of a large number of face images collected under an unrestrained environment with extreme facial variations in expression, pose, and illumination. Experiments and comparison with various methods which include not only the local descriptive methods but also local descriptive-based multiresolution analysis (MRA) based methods demonstrate the efficacy of the LBP-based IDW method.

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Section: Implementation Of 2-d Improved Directional Wavelet (2-d Idw)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Face Identification Using LBP-Based Improved Directional Wavelet Transform

Muqeet¹,

Hameeduddin²

2021

Biometric Systems

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“…Bu nedenle sadece renk ve kenar bilgileri gibi özellikler kullanılarak görüntüleri analiz etmek istenen sonuçları vermemektedir. Bunun sonucu olarak, görüntüleri farklı ve güçlü yaklaşımlar ile analiz eden özellik çıkarma metotları kullanılarak etkin sonuçlar elde edilebilmiştir [1], [2].…”

Section: Introductionunclassified

“…Geliştirilen yöntemler orijinal LBP yönteminin gürültü, dönme ve ışık değişimlerine karşı güçlü kalmasını sağlamıştır. Bunun sonucu olarak yüz tanıma [2], doku sınıflandırma [25], [26], duman tespiti [27] ve medikal görüntü işleme [28] gibi alanlarda yoğun bir şekilde kullanılmaktadır. Heikkila ve diğerleri tarafından geliştirilen centersymmetric LBP (CS-LBP) yöntemi merkez piksel ile komşularını karşılaştırmak yerine piksellerin merkez simetrik çiftlerini karşılaştırmaktır [29].…”

Section: Introductionunclassified

A Novel Texture Classification Method Based on Neutrosophic Truth

Alpaslan

2020

Sakarya University Journal of Computer and Information Sciences

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Texture analysis is one of the basic procedures used in solving problems in computer vision and image processing. In this study, we propose a new local binary pattern (LBP) method based on neutrosophic set. The proposed method is named as the LBP NZ . In the proposed LBP NZ  method, the texture image is converted into a neutrosophic set and the texture image is expressed by truth membership set. The local binary pattern features are calculated, by using the neutrosophic truth set instead of the original input image. The neutrosophic membership sets are more resistant to noise than the original input image. The neutrosophic set suppresses noise components, so that edge information can be calculated more accurately. Thus, utilization of the neutrosophic truth set instead of the original image has provided more effective local binary pattern features. The proposed method is able to achieve high classification accuracy with low feature size, reasonable computational cost. Experimental results show that the proposed method increases the accuracy of the local binary pattern method to the classification by approximately 11% without increasing the feature dimension. The obtained results reveal that the proposed method is applicable for real-time applications.

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“…In such cases, recognition can be achieved with camera images taken in the outdoors, where the targeted person is unaware. Thus, face recognition techniques are widely used in criminal identification applications [8]. In facial recognition systems, users must adhere to the rules as they enter the system with face data.…”

Section: Introductionmentioning

confidence: 99%

Face Recognition with Triangular Fuzzy Set-Based Local Cross Patterns in Wavelet Domain

et al. 2019

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In this study, a new face recognition architecture is proposed using fuzzy-based Discrete Wavelet Transform (DWT) and fuzzy with two novel local graph descriptors. These graph descriptors are called Local Cross Pattern (LCP). The proposed fuzzy wavelet-based face recognition architecture consists of DWT, Triangular fuzzy set transformation, and textural feature extraction with local descriptors and classification phases. Firstly, the LL (Low-Low) sub-band is obtained by applying the 2 Dimensions Discrete Wavelet Transform (2D DWT) to face images. After that, the triangular fuzzy transformation is applied to this band in order to obtain A, B, and C images. The proposed LCP is then applied to the B image. LCP consists of two types of descriptors: Vertical Local Cross Pattern (VLCP) and Horizontal Local Cross Pattern (HLCP). Linear discriminant analysis, quadratic discriminant, analysis, quadratic kernel-based support vector machine (QKSVM), and K-nearest neighbors (KNN) were ultimately used to classify the extracted features. Ten widely used descriptors in the literature are applied to the fuzzy wavelet architecture. AT&T, CIE, Face94, and FERET databases are used for performance evaluation of the proposed methods. Experimental results show that the LCP descriptors have high face recognition performance, and the fuzzy wavelet-based model significantly improves the performances of the textural descriptors-based face recognition methods. Moreover, the proposed fuzzy-based domain and LCP method achieved classification accuracy rates of 97.3%, 100.0%, 100.0%, and 96.3% for AT&T, CIE, Face94, and FERET datasets, respectively.

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Local binary patterns based on directional wavelet transform for expression and pose-invariant face recognition

Cited by 40 publications

References 20 publications

Face Identification Using LBP-Based Improved Directional Wavelet Transform

Face Identification Using LBP-Based Improved Directional Wavelet Transform

A Novel Texture Classification Method Based on Neutrosophic Truth

Face Recognition with Triangular Fuzzy Set-Based Local Cross Patterns in Wavelet Domain

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