论文标题
分布式的大约最近的邻居方法用于实时面部识别
A Distributed Approximate Nearest Neighbor Method for Real-Time Face Recognition
论文作者
论文摘要
如今，面部识别和更普遍的图像识别在现代世界中具有许多应用，并且被广泛用于我们的日常任务中。本文旨在使用涉及大量类的大数据集提出一种分布式的近似邻居（ANN）方法，以实时面部识别。提出的方法基于使用聚类方法将数据集分离为不同的群集，并通过定义群集权重指定每个群集的重要性。为此，使用最大似然方法从每个群集中选择参考实例。这个过程导致更明智的实例选择，因此可以提高算法的性能。实验结果证实了该方法的效率及其在准确性和处理时间方面的表现。
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.
