Efficient large-scale face clustering using an online Mixture of Gaussians

Abstract

In recent years, the number of applications demanding real-time face clustering algorithms has increased, especially for security and surveillance purposes. However, state-of-the-art face clustering methods are offline, they need to repeat the whole clustering process every time new data arrives, and thus, they are not suitable for real-time applications. On the other hand, online clustering methods are highly dependent on the order and the size of the data, and they are less accurate than offline methods. To overcome these limitations, we present an online gaussian mixture-based clustering method (OGMC). The key idea of this method is the proposal that an identity can be represented by more than just one distribution or cluster. Using feature vectors extracted from the incoming faces, OGMC generates clusters that may be connected to others depending on their proximity and their robustness, and updates their connections every time their parameters are updated. With this approach, we reduce the dependency of the clustering process on the order and the size of the data and we are able to deal with complex data distributions. Experimental results show that OGMC outperforms state-of-the-art clustering methods on large-scale face clustering benchmarks not only in accuracy, but also in efficiency and scalability.