Using Deep Relational Features to Verify Kinship

Kinship verification from facial images is a very challenging research topic. Differing from most of previous methods focusing on calculating a similarity metric, in this work, we utilize convolutional neural network and autoencoder to learn deep relational features for verifying kinship from facial images. Specifically, we firstly train a convolutional neural network to extract representative facial features, which derive from the last fully-connected layer in network. Then, facial features from two person are set as two ends of an autoencoder respectively, and relational features are extracted from the middle layer of the trained autoencoder. Finally, SVM classifiers are adopted to verify kinship (e. g., Father-Son). Experimental results on two public datasets show the effectiveness of the approach proposed in this work.