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Bloodhound : Kinship Through Imaging

TLDR
System first detects all the faces from given photo then extracts the features from the faces using Gabor wavelet transform, which ultimately results to the classification of whether kinship or not.
Abstract
There are many social networking web sites used by people and every day numbers of photos are uploaded by them on it. But from a photo we cannot predict the relationship among the people in photo. So there is a need for automatically identifying and predicting relationship, specifically kinship from photo. Proposed system comes under Computer Vision and is based on Face recognition, Feature extraction and knowledge transfer learning. System first detects all the faces from given photo then extracts the features from the faces using Gabor wavelet transform. A UB KinFace version 2.o database is used to train the system giving extracted features as a matrix that is compared with extracted features from photo, which ultimately results to the classification of whether kinship or not.

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Citations
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Proceedings ArticleDOI

Verification of family relation from parents and child facial images

TL;DR: Experimental results have shown that the proposed system can effectively annotate the verification of family relation and observe in the experiments that LBP features perform stably and robustly over a useful range of less resolutions of facial images.
References
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Proceedings ArticleDOI

Rapid object detection using a boosted cascade of simple features

TL;DR: A machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates and the introduction of a new image representation called the "integral image" which allows the features used by the detector to be computed very quickly.

A Survey of Recent Advances in Face Detection

Cha Zhang, +1 more
TL;DR: This technical report surveys the recent advances in face detection for the past decade and surveys the various techniques according to how they extract features and what learning algorithms are adopted.
Journal ArticleDOI

Neighborhood repulsed metric learning for kinship verification

TL;DR: This paper proposes a new neighborhood repulsed metric learning (NRML) method for kinship verification, and proposes a multiview NRM-L method to seek a common distance metric to make better use of multiple feature descriptors to further improve the verification performance.
Proceedings ArticleDOI

Towards computational models of kinship verification

TL;DR: This work conducts a controlled online search to collect frontal face images of 150 pairs of public figures and celebrities, along with images of their parents or children, and proposes and evaluates a set of low-level image features for the challenge of kinship verification.
Journal ArticleDOI

Understanding Kin Relationships in a Photo

TL;DR: Experimental results have shown that the proposed algorithms can effectively annotate the kin relationships among people in an image and semantic context can further improve the accuracy.
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