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
Modeling of facial aging and kinship: A survey
TL;DR: In this paper, the authors provide an up-to-date, complete list of available annotated datasets and an in-depth analysis of geometric, hand-crafted, and learned facial representations that are used for facial aging and kinship characterization.
Journal ArticleDOI
Learning discriminative compact binary face descriptor for kinship verification
TL;DR: Experimental results on three kinship datasets are presented to show the effectiveness of the proposed weakly-supervised feature learning method called discriminative compact binary face descriptor for facial kinship verification.
Journal ArticleDOI
Evaluation of periocular features for kinship verification in the wild
TL;DR: This paper explores the effectiveness of periocular region in verifying kinship from images captured in the wild and proposes a block-based neighborhood repulsed metric learning (BNRML) framework, an extension of NRML, to yield more discriminative power.
Journal ArticleDOI
Supervised Mixed Norm Autoencoder for Kinship Verification in Unconstrained Videos
TL;DR: This research proposes a new deep learning framework for kinship verification in unconstrained videos using a novel Supervised Mixed Norm AutoEncoder (SMNAE), which introduces class-specific sparsity in the weight matrix.
Journal ArticleDOI
Mixed bi-subject kinship verification via multi-view multi-task learning
TL;DR: This work introduces a new type of learning problem, called mixed bi-subject kinship verification, and proposes a novel multi-task learning method to address this problem with two transformation matrices - one is shared amongst all the tasks and the other is unique to each task.
References
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