Recognizing Families In the Wild (RFIW) is a large-scale kinship recognition challenge based on the FIW dataset. This dataset is the largest databases for kinship recognition, consisting of more than 13,000 family photos and 1,000 families. The number of members in each family range from 4 to 38. One of the tasks for the database is, given photos of two individuals, predict whether they have any kin relationship or not. In this paper, we present a deep learning approach using Siamese Convolutional Neural Network Architecture to quantify the similarity between two given photos. We use two parallel SqueezeNet Networks, initialized with weights obtained after training the SqueezeNet on the VGGFace2 Dataset, and use a similarity metric and fully connected networks to merge the two networks to a single output. We use different similarity metric such as L1 norm, L2 Norm, and Cosine Similarity. Our network gives good accuracy and AUC scores.
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