Early and late features fusion for kinship verification based on constraint selection

Van, Tien Nguyen; Hoang, Vinh Truong

doi:10.1109/apcc47188.2019.9026554

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…While Wang et al [10] did take age gaps into account, they also needed to introduce GAN as an intermediate domain. There are also many other related methods [12,14,30,38,39] that have been pro-posed. Overall, compared with traditional methods, deep models are better able to extract representative and comprehensive features that are difficult to select by hand.…”

Section: Deep Learning-based Face Feature Learningmentioning

confidence: 99%

Age-Invariant Adversarial Feature Learning for Kinship Verification

Fan

Yang

et al. 2022

Mathematics

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Kinship verification aims to determine whether two given persons are blood relatives. This technique can be leveraged in many real-world scenarios, such as finding missing people, identification of kinship in forensic medicine, and certain types of interdisciplinary research. Most existing methods extract facial features directly from given images and examine the full set of features to verify kinship. However, most approaches are easily affected by the age gap among faces, with few methods taking age into account. This paper accordingly proposes an Age-Invariant Adversarial Feature learning module (AIAF), which is capable of factoring in full facial features to create two uncorrelated components, i.e., identity-related features and age-related features. More specifically, we harness a type of adversarial mechanism to make the correlation between these two components as small as possible. Moreover, to pay different attention to identity-related features, we present an Identity Feature Weighted module (IFW). Only purified identity features are fed into the IFW module, which can assign different weights to the features according to their importance in the kinship verification task. Experimental results on three public popular datasets demonstrate that our approach is able to capture useful age-invariant features, i.e., identity features, and achieve significant improvements compared with other state-of-the-art methods on both small-scale and large-scale datasets.

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Section: Deep Learning-based Face Feature Learningmentioning

confidence: 99%

Age-Invariant Adversarial Feature Learning for Kinship Verification

Fan

Yang

et al. 2022

Mathematics

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“…This approach has been shown its effectiveness in kinship verification. Van and Hoang extract HOG features for coding facial image in different color spaces [32,33].…”

Section: Introductionmentioning

confidence: 99%

A Revisit Histogram of Oriented Descriptor for Facial Color Image Classification Based on Fusion of Color Information

Quoc

Hoang

2021

Journal of Sensors

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Histogram of Oriented Gradient (HOG) is a robust descriptor which is widely used in many real-life applications, including human detection, face recognition, object counting, and video surveillance. In order to extract HOG descriptor from color images whose information is three times more than the grayscale images, researchers currently apply the maximum magnitude selection method. This method makes the information of the resulted image is reduced by selecting the maximum magnitudes. However, after we extract HOG using the unselected magnitudes of the maximum magnitude selection method, we observe that the performance is better than using the maximum magnitudes in several cases. Therefore, in this paper, we propose a novel approach for extracting HOG from color images such as Color Component Selection and Color Component Fusion. We also propose the extended kernels in order to improve the performance of HOG. With our new approaches in the color component analysis, the experimental results of several facial benchmark datasets are enhanced with the increment from 3 to 10% of accuracy. Specifically, a 95.92% of precision is achieved on the Face AR database and 75% on the Georgia Face database. The results are better more than 10 times compared with the original HOG approach.

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Adaptive Ternary Pattern Based on Supervised Learning Approach for Ground-Based Cloud Type Classification

Hoang

2020

Advances in Intelligent Systems and Computing

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Early and late features fusion for kinship verification based on constraint selection

Cited by 5 publications

References 33 publications

Age-Invariant Adversarial Feature Learning for Kinship Verification

Age-Invariant Adversarial Feature Learning for Kinship Verification

A Revisit Histogram of Oriented Descriptor for Facial Color Image Classification Based on Fusion of Color Information

Adaptive Ternary Pattern Based on Supervised Learning Approach for Ground-Based Cloud Type Classification

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