Proceedings ArticleDOI
Self-similarity representation of Weber faces for kinship classification
Naman Kohli,Richa Singh,Mayank Vatsa +2 more
- pp 245-250
TLDR
A kinship classification algorithm that uses the local description of the pre-processed Weber face image to outperforms an existing algorithm and yields a classification accuracy of 75.2%.Abstract:
Establishing kinship using images can be utilized as context information in different applications including face recognition. However, the process of automatically detecting kinship in facial images is a challenging and relatively less explored task. The reason for this includes limited availability of datasets as well as the inherent variations amongst kins. This paper presents a kinship classification algorithm that uses the local description of the pre-processed Weber face image. A kinship database is also prepared that contains images pertaining to 272 kin pairs. The database includes images of celebrities (and their kins) and has four ethnicity groups and seven kinship groups. The proposed algorithm outperforms an existing algorithm and yields a classification accuracy of 75.2%.read more
Citations
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Journal ArticleDOI
Kinship verification using multi-level dictionary pair learning for multiple resolution images
Aarti Goyal,Toshanlal Meenpal +1 more
TL;DR: Wang et al. as discussed by the authors proposed a multi-level dictionary pair learning (MLDPL) method to learn dictionary pairs by incorporating multiple resolution images for kinship verification by transforming discriminative features of image pairs into different coding coefficients in the same space, thereby reducing the differences between them.
Journal ArticleDOI
Easy pair selection method for Kinship Verification using fixed age group images
TL;DR: Zhang et al. as mentioned in this paper proposed a new scheme called easy-pair selection method for kV, which categorized the non-kin pairs into easy and hard pairs based on sum of square distance with respect to the feature space of the pairs.
Dissertation
Image-based family verification in the wild
TL;DR: This paper proposes a flexible pipeline composed by modules that assembled together improve the results obtained individually and improves state-of-the-art results that do not use external data for the two public databases Kin face-I and KinFace-II.
Book ChapterDOI
Introduction to Facial Kinship Verification
Haibin Yan,Jiwen Lu +1 more
TL;DR: In this chapter, the background of facial kinship verification is introduced and the state-of-the-art of facial relatives verification is reviewed, to outline the organization of the book.
Journal ArticleDOI
A survey on kinship verification
TL;DR: The Nemo-Kinship dataset as discussed by the authors was proposed as a benchmark dataset addressing large inter-subject age variations and consisting of 4216 videos of 248 persons from 85 families.
References
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