Robust local binary pattern for face recognition in different challenges

Karanwal, Shekhar

doi:10.1007/s11042-022-13006-8

Cited by 19 publications

(4 citation statements)

References 36 publications

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“…In this regard, LPB does not capture the details of microstructures and it is sensitive to image noise. (Karanwal, 2022) Extracting and classifying local image features is a fundamental challenge in many applications (Liu, Yang, & Huang, 2011) and different approaches have been used to describe local image patterns. Some of the descriptors are Shape Context (Salve & Jondhale, 2010), spin image (Lazebnik, Schmid, & Ponce, 2003), complex filters (Schaffalitzky & Zisserman, 2002), steerable filters (Freeman & Adelson, 1991), moment invariants (Van Gool, Moons, & Ungureanu, 1996), SIFT (Lowe, 2004), and the differential invariants (Koenderink & Van Doorn, 1987).…”

Section: Robust Lbb (Rlbp)mentioning

confidence: 99%

A Multi Feature Fusion Search for Facial Expression Recognition Based on Classical Extraction Methods

Appati,

Wunake

2024

Preprint

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The ability to recognize emotional expressions from faces has become an essential component of human-computer interaction (HCI). Recently Oriented FAST and rotated BRIEF (ORB) and Local Binary Patterns (LBP) was used to overcome the limitations of DNN excessive hardware specifications requirements, considering the low hardware specifications used in real-world scenarios. There still exists drawbacks with LBP and ORB, in that LBP is not as resistant to image noise. LBP descriptors are invariant to changing lighting conditions and partial occlusion. Also, when a fixed threshold is utilized under challenging lighting conditions, the ORB algorithm is constrained by its incapacity to extract feature points. We propose a Multi Feature Fusion For Facial Expression Recognition using the algorithms Scale Invariant Feature Transform (SIFT), Histogram Oriented Gradient (HOG), ORB, and LBP. This study proposes a combinatorial blending of least three of these algorithms by looking at the merits of one over the other, also to obtain a novel technique out of the combinatorial schemes, and still obtain better performance of the recognition rates. The proposed method was evaluated on the Extended Cohn Kanade (CK+) and Japanese Famele Facial Expression (JAFFE), and the 2013 Facial Expression Recognition (FER2013) datasets. Based on the merits of our proposed feature extraction schemes, this study explored their respective feature extractions to obtain their individual extracted features from the descriptors. The individual features were then fused together to obtain our multi fused feature, the fused features were then passed onto the classifier for training of our models and image recognitions tasks. This study showed that the proposed algorithm performed well compared to existing state of the art.

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Section: Robust Lbb (Rlbp)mentioning

confidence: 99%

A Multi Feature Fusion Search for Facial Expression Recognition Based on Classical Extraction Methods

Appati,

Wunake

2024

Preprint

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“…LaplacianFaces [33] consists in mapping the face into a face subspace based on Locality Preserving Projections (LPP) [32] to get the best global face description. The most popular used techniques are based on different descriptors, such as Local Binary Pattern (LBP) and its derivatives [47], Histogram of Oriented Gradients (HOG) [55], Vander Lugt Correlator (VLC) , Scale Invariant Feature Transform (SIFT). All these descriptors are presented in [78].…”

Section: Face Recognition Studiesmentioning

confidence: 99%

68 Landmarks are Efficient for 3D Face Alignment: What about More? 3D Face Alignment Method Applied to Face Recognition

Jabberi¹,

Wali²,

Chaudhuri³

et al. 2021

Preprint

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“…Texture-based methods, such as double Gabor orientation Weber local descriptor (DG-WLD) [5], multi-scale Gabor orientation Weber local descriptors (MOGWLD) [6], difference 2 of 17 of block means (DBM) [7], democratic voting down-sampling (DVD) [8], and various local binary pattern [9] (LBP) variants mentioned in [10], extract information about the direction, frequency, and phase of palm vein texture as features for matching and recognition. However, these methods are limited by the inadequate richness and clarity of texture information in palm vein images, which can result in decreased recognition performance.…”

Section: Introductionmentioning

confidence: 99%

Identity Recognition System Based on Multi-Spectral Palm Vein Image

Wu,

Li,

Zhang

et al. 2023

Electronics

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A multi-spectral palm vein image acquisition device based on an open environment has been designed to achieve a highly secure and user-friendly biometric recognition system. Furthermore, we conducted a study on a supervised discriminative sparse principal component analysis algorithm that preserves the neighborhood structure for palm vein recognition. The algorithm incorporates label information, sparse constraints, and local information for effective supervised learning. By employing a robust neighborhood selection technique, it extracts discriminative and interpretable principal component features from non-uniformly distributed multi-spectral palm vein images. The algorithm addresses challenges posed by light scattering, as well as issues related to rotation, translation, scale variation, and illumination changes during non-contact image acquisition, which can increase intra-class distance. Experimental tests are conducted using databases from the CASIA, Tongji University, and Hong Kong Polytechnic University, as well as a self-built multi-spectral palm vein dataset. The results demonstrate that the algorithm achieves the lowest equal error rates of 0.50%, 0.19%, 0.16%, and 0.1%, respectively, using the optimal projection parameters. Compared to other typical methods, the algorithm exhibits distinct advantages and holds practical value.

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Robust local binary pattern for face recognition in different challenges

Cited by 19 publications

References 36 publications

A Multi Feature Fusion Search for Facial Expression Recognition Based on Classical Extraction Methods

A Multi Feature Fusion Search for Facial Expression Recognition Based on Classical Extraction Methods

68 Landmarks are Efficient for 3D Face Alignment: What about More? 3D Face Alignment Method Applied to Face Recognition

Identity Recognition System Based on Multi-Spectral Palm Vein Image

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