Measure face similarity based on deep learning
Abstract: Measuring face similarity is a task in computer vision that is different from face recognition. It aims to find an embedding in which similar faces have a smaller distance than dissimilar ones. This project investigates two different Siamese networks to explore whether these specific networks outperform face recognition methods on face similarity. The best accuracy is from a Siamese convolution neural network, which is 65.11%. Moreover, the best results in a similarity ranking task are obtained from Siamese geometry-aware metric learning. Besides, this project creates a novel dataset with facial image pairs for face similarity.
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