Abstract

The goal of the study is to increase the computation efficiency of the face recognition that uses feature vectors to describe facial images on photos and videos. These high-dimensional feature vectors are nowadays produced by convolutional neural networks. The methods to aggregate the features generated for each video frame are used to process the video sequences. A novel hierarchical recognition algorithm is proposed. In contrast to known approaches our algorithm seeks the nearest neighbors only among reference images of most reliable classes selected at the preceding stage to carry out the sequential analysis of a more detailed description level (with a greater dimensionality of the feature vector). At each stage principal components are compared, the number of the components being chosen according to a given portion of explained variations. Datasets like Labeled Faces in the Wild, YouTubeFaces, IARPA Janus Benchmark C and different neural-net face descriptors are used to compare the algorithm with other methods. In contrast with the conventional nearest-neighbor method, the proposed approach is shown to allow a 2- to 16-times reduction of the classifier running time.

中文翻译：

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高维神经网络特征序列分析的高效计算人脸识别算法

摘要

该研究的目的是提高使用特征向量描述照片和视频上的面部图像的面部识别的计算效率。如今，这些高维特征向量是由卷积神经网络产生的。聚合为每个视频帧生成的特征的方法用于处理视频序列。提出了一种新颖的层次识别算法。与已知方法相反，我们的算法仅在前一阶段选择的最可靠类别的参考图像中寻找最近的邻居，以对更详细的描述级别（具有较大特征向量维）进行顺序分析。在每个阶段，对主要成分进行比较，根据所解释的变化的给定部分选择成分的数量。诸如“狂野中的带标签的面孔”，YouTubeFaces，IARPA Janus Benchmark C和不同的神经网络面孔描述符之类的数据集用于将该算法与其他方法进行比较。与传统的最近邻居方法相比，所建议的方法显示出可将分类器运行时间减少2到16倍。