Kinship recognition is a prominent research aiming to find if kinship relation exists between two different individuals. In general, child closely resembles his/her parents more than others based on facial similarities. These similarities are due to genetically inherited facial features that a child shares with his/her parents. Most existing researches in kinship recognition focus on full facial images to find these kinship similarities. This paper first presents kinship recognition for similar full facial images using proposed Global-based dual-tree complex wavelet transform (G-DTCWT). We then present novel patch-based kinship recognition methods based on dual-tree complex wavelet transform (DT-CWT): Local Patch-based DT-CWT (LP-DTCWT) and Selective Patch-Based DT-CWT (SP-DTCWT). LP-DTCWT extracts coefficients for smaller facial patches for kinship recognition. SP-DTCWT is an extension to LP-DTCWT and extracts coefficients only for representative patches with similarity scores above a normalized cumulative threshold. This threshold is computed by a novel patch selection process. These representative patches contribute more similarities in parent/child image pairs and improve kinship accuracy. Proposed methods are extensively evaluated on different publicly available kinship datasets to validate kinship accuracy. Experimental results showcase efficacy of proposed methods on all kinship datasets. SP-DTCWT achieves competitive accuracy to state-of-the-art methods. Mean kinship accuracy of SP-DTCWT is 95.85% on baseline KinFaceW-I and 95.30% on KinFaceW-II datasets. Further, SP-DTCWT achieves the state-of-the-art accuracy of 80.49% on the largest kinship dataset, Families In the Wild (FIW).

中文翻译：

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基于补丁的双树复小波变换用于亲属识别

亲属识别是一项重要的研究，旨在发现两个不同个体之间是否存在亲属关系。通常，基于面部相似性，孩子比其他人更像他/她的父母。这些相似之处是由于孩子与父母共享的遗传遗传的面部特征。亲属识别的大多数现有研究都集中在全脸图像上，以找到这些亲属相似之处。本文首先提出了使用提议的基于全局的双树复小波变换（G-DTCWT）对相似的全脸图像进行亲属识别。然后，我们提出基于双树复小波变换（DT-CWT）的新颖的基于补丁的亲属识别方法：基于本地补丁的DT-CWT（LP-DTCWT）和基于选择性补丁的DT-CWT（SP-DTCWT）。LP-DTCWT提取较小的面部补丁的系数，以进行血缘关系识别。SP-DTCWT是LP-DTCWT的扩展，仅针对相似性评分高于标准化累积阈值的代表性补丁提取系数。该阈值通过新颖的补丁选择过程来计算。这些代表性补丁在父/子图像对中提供了更多相似性，并提高了亲缘关系准确性。建议的方法在不同的公开亲属数据集中进行了广泛评估，以验证亲属准确性。实验结果证明了所提出方法对所有亲属数据集的有效性。SP-DTCWT与最新方法相比具有竞争优势。在基线KinFaceW-I上，SP-DTCWT的平均亲属准确度为95.85％，在KinFaceW-II数据集上为95.30％。进一步，