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Patch-Based Dual-Tree Complex Wavelet Transform for Kinship Recognition.

Aarti Goyal, Toshanlal Meenpal

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|November 2, 2020

Summary

This summary is machine-generated.

This study introduces new methods for kinship recognition using facial image analysis. Selective Patch-Based Dual-Tree Complex Wavelet Transform (SP-DTCWT) significantly improves accuracy in identifying parent-child relationships.

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Area of Science:

Computer Vision
Biometrics
Pattern Recognition

Background:

Kinship recognition aims to identify familial relationships between individuals.
Facial similarities, inherited genetically, are key indicators of kinship.
Existing methods often rely on full facial images, limiting recognition accuracy.

Purpose of the Study:

To develop and evaluate novel patch-based methods for kinship recognition.
To enhance accuracy by focusing on representative facial features.
To validate the proposed methods on diverse kinship datasets.

Main Methods:

Global-based Dual-Tree Complex Wavelet Transform (G-DTCWT) for full facial images.
Local Patch-based DT-CWT (LP-DTCWT) for analyzing smaller facial regions.
Selective Patch-Based DT-CWT (SP-DTCWT) using a novel patch selection process for representative features.

Main Results:

SP-DTCWT demonstrated high accuracy on KinFaceW-I (95.85%) and KinFaceW-II (95.30%).
Achieved state-of-the-art accuracy of 80.49% on the large Families In the Wild (FIW) dataset.
Proposed methods showed consistent efficacy across multiple kinship datasets.

Conclusions:

Patch-based approaches, particularly SP-DTCWT, are effective for kinship recognition.
Focusing on representative facial patches enhances the identification of genetic similarities.
The developed methods offer competitive and state-of-the-art performance in kinship verification.