A Novel Kinship Verification Method Based on Deep Transfer Learning and Feature Nonlinear Mapping



There are some problems when the discriminative features are used in the traditional kinship verification methods, such as focusing on the local region information, containing a lot of noisy in non-face regions and redundant information in overlapping regions, manual parameters setting and high dimension. To solve the above problems, a novel kinship verification method based on deep transfer learning and feature nonlinear mapping is proposed in this paper. Firstly, a new deep learning model trained on the face recognition dataset is transferred to the kinship datasets to extract high-level feature. Secondly, siamese multi-layer perceptrons and triangular similarity metric learning are combined to reduce the dimensionality of feature vector by nonlinear mapping. Meanwhile it would guarantee a smaller distance between kin pairs while a larger distance between non-kin pairs. Lastly, the cosine similarity of feature vector pairs is computed, and traditional classifier, such as SVM, is used. Experiments on the TSKinFace, KinFace W-I and KinFace W-II datasets indicate the proposed method could achieve better performance than the traditional methods.


Kinship Verification; Deep Transfer Learning; Feature Nonlinear Mapping; Siamese Multi-Layer Perceptrons; Triangular Similarity Metric Learning



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