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Computation-Efficient Face Recognition Algorithm Using a Sequential Analysis of High Dimensional Neural-Net Features

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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.

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Funding

This research was prepared within the framework of the Academic Fund Program at the National Research University Higher School of Economics (HSE University) in 2019 (grant no. 19-04-004) and within the framework of the Russian Academic Excellence Project “5-100”.

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Authors and Affiliations

  1. Laboratory of Algorithms and Technologies for Network Analysis, National Research University Higher School for Economics, 603014, Nizhny Novgorod, Russia

    A. D. Sokolova & A. V. Savchenko

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  1. A. D. Sokolova
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  2. A. V. Savchenko
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Correspondence to A. D. Sokolova or A. V. Savchenko.

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Sokolova, A.D., Savchenko, A.V. Computation-Efficient Face Recognition Algorithm Using a Sequential Analysis of High Dimensional Neural-Net Features. Opt. Mem. Neural Networks 29, 19–29 (2020). https://doi.org/10.3103/S1060992X2001004X

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