Imagebased Kinship Verification using Fusion Convolutional Neural Network In this paper, we investigate the performance of fusion convolutional neural network (CNN) classifier for imagebased kinship verification problem. Two fusion configurations were used for the experiments, early fusion CNN classifier and late fusion CNN classifier. The early fusion configuration of the CNN classifier takes combined two face images as input for verification. The advantages of early fusion configuration are no heavy changes in the classifier architecture and only the first layer that have a different filter size. The late fusion configuration of the CNN classifier formed by creating dual CNN network for extracting the deep features of each face image and classify the kinship relationship using two fullyconnected layers. The softmax and angular softmax (asoftmax) loss are used for evaluating the network in the training process with finetuning strategy. The classifier then evaluated using largescale FIW (Family in the Wild) kinship verification dataset consists of 1,000 family and 11 different kinship relationship. Experiments using the 5fold configuration on FIW dataset show that the ensemble of fusion CNN classifier produces comparable performance with several different stateoftheart methods.

Comparative Convolutional Neural Network for Younger Face Identification We consider the problem of determining whether a pair of face images can be distinguishable in terms of age and if so, which is the younger of the two. We also determine the degree of distinguishability in which age differences are categorized into large, medium, small and tiny. We propose a comparative convolutional neural network combining two parallel deep architectures. Based on the two deep learnt face features, we introduce a comparative layer to represent their mutual relationships, followed by a concatenatation implementation. Softmax is adopted to complete the classification task. To demonstrate our approach, we construct a very large dataset consisting of over 1.7 million face image pairs with youngold labels.