A Distributed Approximate Nearest Neighbor Method for Real-Time Face Recognition
This work addresses the need for efficient face recognition in applications like security and daily tasks, but it is incremental as it builds on existing ANN techniques with clustering enhancements.
The paper tackled the problem of real-time face recognition on large datasets by proposing a distributed approximate nearest neighbor method that uses clustering and weighted reference instance selection. Experimental results showed improved accuracy and reduced processing time compared to existing methods.
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.