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A weighted exponential discriminant analysis through side-information for face and kinship verification using statistical binarized image features
International Journal of Machine Learning and Cybernetics ( IF 3.1 ) Pub Date : 2020-07-11 , DOI: 10.1007/s13042-020-01163-x
Oualid Laiadi , Abdelmalik Ouamane , Abdelhamid Benakcha , Abdelmalik Taleb-Ahmed , Abdenour Hadid

Side-information based exponential discriminant analysis (SIEDA) is more efficient than side-information based linear discriminant analysis (SILDA) in computing the discriminant vectors because it maximizes the Fisher criterion function. In this paper, we develop a novel criterion, named side-information based weighted exponential discriminant analysis (SIWEDA), that is based on the classical SIEDA method. We reformulate and generalize the classical Fisher criterion function in order to maximize it, with the property to pull as close as possible the intra-class samples (within-class samples), and push and repulse away as far as possible the inter-class samples (between-class samples). Thus, SIWEDA selects the eigenvalues of high significance and eliminate those with less discriminative information. To reduce the feature vector dimensionality and lighten the class intra-variability, we use SIWEDA and within class covariance normalization (WCCN) using the proposed statistical binarized image features (StatBIF). Moreover, we use score fusion strategy to extract the complementarity of different weighting scales of our StatBIF descriptor. We conducted experiments to evaluate the performance of the proposed method under unconstrained environment, using five datasets namely LFW, YTF, Cornell KinFace, UB KinFace and TSKinFace datasets, in the context of matching faces and kinship verification in the wild conditions. The experiments showed that the proposed approach outperforms the current state of the art. Very interestingly, our approach showed superior performance compared to methods based on deep metric learning.



中文翻译:

使用统计二值化图像特征通过侧面信息进行面部和亲属关系验证的加权指数判别分析

在计算判别向量时,基于边信息的指数判别分析(SIEDA)比基于边信息的线性判别分析(SILDA)更有效,因为它最大化了Fisher准则函数。在本文中,我们开发了一种基于经典SIEDA方法的新标准,即基于边信息的加权指数判别分析(SIWEDA)。我们重新构造并泛化了经典的Fisher准则函数以使其最大化,其特性是尽可能拉近类内样本(类内样本),并尽可能推挤和排斥类间样本(类间样本)。因此,SIWEDA选择具有高重要性的特征值,并消除具有较少判别信息的特征值。为了降低特征向量的维数并减轻类内变异性,我们使用SIWEDA并使用建议的统计二值化图像特征(StatBIF)在类协方差归一化(WCCN)中。此外,我们使用分数融合策略来提取StatBIF描述符的不同加权尺度的互补性。我们进行了实验,以在野外条件下进行面部匹配和亲缘关系验证的情况下,使用LFW,YTF,Cornell KinFace,UB KinFace和TSKinFace数据集这五个数据集,在不受限制的环境下评估了该方法的性能。实验表明,所提出的方法优于现有技术。非常有趣的是,与基于深度度量学习的方法相比,我们的方法表现出了卓越的性能。我们使用SIWEDA并使用建议的统计二值化图像特征(StatBIF)在类协方差归一化(WCCN)中。此外,我们使用分数融合策略来提取StatBIF描述符的不同加权尺度的互补性。我们进行了实验,以在野外条件下进行面部匹配和亲缘关系验证的情况下,使用LFW,YTF,Cornell KinFace,UB KinFace和TSKinFace数据集这五个数据集,在不受限制的环境下评估了该方法的性能。实验表明,所提出的方法优于现有技术。非常有趣的是,与基于深度度量学习的方法相比,我们的方法表现出了卓越的性能。我们使用SIWEDA并使用建议的统计二值化图像特征(StatBIF)在类协方差归一化(WCCN)中。此外,我们使用分数融合策略来提取StatBIF描述符的不同加权尺度的互补性。我们进行了实验,以在野外条件下进行面部匹配和亲缘关系验证的情况下,使用LFW,YTF,Cornell KinFace,UB KinFace和TSKinFace数据集这五个数据集,在不受限制的环境下评估了该方法的性能。实验表明,所提出的方法优于现有技术。非常有趣的是,与基于深度度量学习的方法相比,我们的方法表现出了卓越的性能。

更新日期:2020-07-13
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