A new method color MS-BSIF Features learning for the robust kinship verification

TL;DR

This paper introduces a novel color MS-BSIF and MS-LBP feature learning method combined with TXQDA for robust kinship verification from facial images, demonstrating superior performance over existing techniques.

Contribution

The paper proposes a new feature learning approach using color MS-BSIF and MS-LBP combined with TXQDA for improved kinship verification accuracy.

Findings

Outperforms state-of-the-art kinship verification methods

Enhances robustness and efficiency in facial kinship recognition

Proven effective on the Kinface Cornell database

Abstract

the paper presents a new method color MS-BSIF learning and MS-LBP for the kinship verification is the machine's ability to identify the genetic and blood the relationship and its degree between the facial images of humans. Facial verification of kinship refers to the task of training a machine to recognize the blood relationship between a pair of faces parent and non-parent (verification) based on features extracted from facial images, and determining the exact type or degree of this genetic relationship. We use the LBP and color BSIF learning features for the comparison and the TXQDA method for dimensionality reduction and data classification. We let's test the kinship facial verification application is namely the kinface Cornell database. This system improves the robustness of learning while controlling efficiency. The experimental results obtained and compared to other methods have proven the reliability of our framework and surpass the performance of other state-of-the-art techniques.