Kinship Verification based on Local Binary Pattern features coding in different color space

Van, Tien Nguyen; Hoang, Vinh Truong

doi:10.1109/ict.2019.8798781

Cited by 12 publications

(5 citation statements)

References 37 publications

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“…Their evaluation results on KFW-I and KFW-II datasets were 75.98% and 77.20%, respectively. Van and Hoang 23 introduced LBP features coding in different color spaces for verifying kinship. After that, the features were computed by Chi-square distance.…”

Section: Handcrafted (Hc) Feature-based Studiesmentioning

confidence: 99%

“…It is characterized by being a rotation invariant method 6 . Also, it is considered one of the most often utilized descriptors in comparison to the local and global descriptors that were previously proposed because of how rapidly and easily it can be computed 23 . In LBP, eight neighboring pixels are compared to each center pixel.…”

Section: Local Binary Pattern (Lbp)mentioning

confidence: 99%

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Enhanced kinship verification analysis based on color and texture handcrafted techniques

Nader

El-Gamal

Elmogy

2022

Preprint

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Nowadays, kinship verification is considered an attractive research area with a great interest in computer vision. It significantly affects applications in the real world, such as finding missing individuals, forensics, and genealogical research. However, verifying kinship relations between people using facial images is not straightforward. Many limitations affect kinship verification accuracy. Therefore, this paper proposes a new approach for verifying kinship based on facial image analysis. The proposed approach goes into six stages: preprocessing, feature extraction, feature normalization, feature fusion, feature representation, and kinship verification. The preprocessing stage is responsible for converting RGB images into other color models. Different types of handcrafted feature descriptors (i.e., color and texture descriptors) are extracted in the feature extraction stage. The texture features are represented by scale invariant feature transform (SIFT), local binary pattern (LBP), and heterogeneous auto-similarities of characteristics (HASC), whereas the color features are represented by color correlogram (CC) and dense color histogram (DCH). Then, all the features are set to the same range in the feature normalization stage to be suitable for feature fusion. The feature fusion stage takes place where the different types of features are concatenated together. Next, in the feature representation stage, the parent and child features are gathered into one feature vector. Finally, the kinship verification stage produces the final decision of being kin or non-kin using the gentle AdaBoost ensemble classifier. KinFaceW-I and KinFaceW-II datasets were used to evaluate the proposed approach, where the obtained results were 79.54\% and 90.65\%, respectively. It is noteworthy that the proposed approach outperforms many state-of-the-art approaches that verify kinship, including those dependent on metric learning and deep convolutional neural nets (CNNs).

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Section: Handcrafted (Hc) Feature-based Studiesmentioning

confidence: 99%

Section: Local Binary Pattern (Lbp)mentioning

confidence: 99%

Enhanced kinship verification analysis based on color and texture handcrafted techniques

Nader

El-Gamal

Elmogy

2022

Preprint

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“…LBP transforms grayscale image into encoded image ( Mukherjee & Meenpal, 2019 ). It is one of the most popular descriptors since it is computed quickly and easily compared to the local and global descriptors proposed in the literature ( Van & Hoang, 2019 ). Each central pixel is compared to its eight neighbours.…”

Section: Kvr Stagesmentioning

confidence: 99%

Kinship verification and recognition based on handcrafted and deep learning feature-based techniques

Nader

El-Gamal

El–Sappagh

et al. 2021

PeerJ Computer Science

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Background and Objectives Kinship verification and recognition (KVR) is the machine’s ability to identify the genetic and blood relationship and its degree between humans’ facial images. The face is used because it is one of the most significant ways to recognize each other. Automatic KVR is an interesting area for investigation. It greatly affects real-world applications, such as searching for lost family members, forensics, and historical and genealogical studies. This paper presents a comprehensive survey that describes KVR applications and kinship types. It presents a literature review of current studies starting from handcrafted passing through shallow metric learning and ending with deep learning feature-based techniques. Furthermore, kinship mostly used datasets are discussed that in turn open the way for future directions for the research in this field. Also, the KVR limitations are discussed, such as insufficient illumination, noise, occlusion, and age variations problems. Finally, future research directions are presented, such as age and gender variation problems. Methods We applied a literature survey methodology to retrieve data from academic databases. An inclusion and exclusion criteria were set. Three stages were followed to select articles. Finally, the main KVR stages, along with the main methods in each stage, were presented. We believe that surveys can help researchers easily to detect areas that require more development and investigation. Results It was found that handcrafted, metric learning, and deep learning were widely utilized in kinship verification and recognition problem using facial images. Conclusions Despite the scientific efforts that aim to address this hot research topic, many future research areas require investigation, such as age and gender variation. In the end, the presented survey makes it easier for researchers to identify the new areas that require more investigation and research.

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“…Histograms of oriented gradient (HOG) descriptor is applied for different problems in machine vision [26][27][28][29][30][31][32]. HOG feature is extracted by counting the occurrences of gradient orientation base on the gradient angle and the gradient magnitude of local patches of an image.…”

Section: The Feature Extraction and Selection 21 Histograms Of Oriementioning

confidence: 99%

Vehicle logo recognition using histograms of oriented gradient descriptor and sparsity score

2020

Self Cite

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Most of vehicle have the similar structures and designs. It is extremely complicated and difficult to identify and classify vehicle brands based on their structure and shape. As we require a quick and reliable response, so vehicle logos are an alternative method of determining the type of a vehicle. In this paper, we propose a method for vehicle logo recognition based on feature selection method in a hybrid way. Vehicle logo images are first characterized by Histograms of Oriented Gradient descriptors and the final features vector are then applied feature selection method to reduce the irrelevant information. Moreover, we release a new benchmark dataset for vehicle logo recognition and retrieval task namely, VLR-40. The experimental results are evaluated on this database which show the efficiency of the proposed approach.

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Kinship Verification based on Local Binary Pattern features coding in different color space

Cited by 12 publications

References 37 publications

Enhanced kinship verification analysis based on color and texture handcrafted techniques

Enhanced kinship verification analysis based on color and texture handcrafted techniques

Kinship verification and recognition based on handcrafted and deep learning feature-based techniques

Vehicle logo recognition using histograms of oriented gradient descriptor and sparsity score

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