Incremental methods in face recognition: a survey

Abstract

Face Recognition has rapidly grown as a commercial requirement for a variety of applications in recent years. There are certain situations in which all the face images may not be available before training or the face images may be distributed at geographically apart locations. Incremental face recognition addresses these problems and possesses certain advantages i.e. being time efficient and dynamic model updation allows addition/deletion of samples on the fly. In this paper, a comprehensive review on the Incremental learning algorithms that are aimed at Face Recognition or tested over Face datasets. The contribution of this paper is three-fold: (a) a novel taxonomy of the Incremental methods have been proposed (b) a review of the face datasets used in Incremental face recognition have been carried out and (c) a performance analysis of the Incremental face recognition methods over various face datasets is also presented. Important conclusions have been drawn that will help the researchers in making suitable choices amongst various methods and datasets. This survey shall act as a useful reference to the researchers and practitioners working in incremental face recognition. Furthermore, several viable research directions have been given at the end.

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Madhavan, S., Kumar, N. Incremental methods in face recognition: a survey. Artif Intell Rev 54, 253–303 (2021). https://doi.org/10.1007/s10462-019-09734-3

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Keywords

  • Supervised
  • Unsupervised
  • Semi-supervised
  • Learning
  • Hybrid
  • Taxonomy
  • Datasets
  • Performance