  Nowadays, face recognition and more generally image recognition have many
applications in the modern world and are widely used in our daily tasks. This
paper aims to propose a distributed approximate nearest neighbor (ANN) method
for real-time face recognition using a big dataset that involves a lot of
classes. The proposed approach is based on using a clustering method to
separate the dataset into different clusters and on specifying the importance
of each cluster by defining cluster weights. To this end, reference instances
are selected from each cluster based on the cluster weights using a maximum
likelihood approach. This process leads to a more informed selection of
instances, so it enhances the performance of the algorithm. Experimental
results confirm the efficiency of the proposed method and its out-performance
in terms of accuracy and the processing time.
