Abstract
Face recognition has become popular in the last few decades among researchers across the globe due to its applicability in several domains. This problem becomes more challenging when only a single training image is available and is popularly known as single sample face recognition (SSFR) problem. SSFR becomes even more complex when images are captured under varying illumination conditions, different poses, occlusion, and expression. Further, deep learning methods have shown performance at par with humans recently. Due to the emergence of deep learning methods in the last decade, it has been made possible to recognize faces with excellent accuracy even in a single sample scenario. In this paper, we present a comprehensive survey of SSFR using deep learning. We also propose a novel taxonomy and broadly divide these methods into three categories viz. virtual sample generation, feature-based, and hybrid methods. Performance comparison of these methods as reported in the literature has also been performed. Finally, we review publicly available databases used by the researchers and give some important future research directions which will help aspiring researchers in this fascinating area.
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Data availibility
Data sharing not applicable to this article as no databases were generated during the current study. The databases analyzed here are publicly available and corresponding references have been given in the text.
Abbreviations
- AI/ML:
-
Artificial intelligence/machine learning
- ANN:
-
Artificial neural network
- CNN:
-
Convolutional neural network
- CPU:
-
Central processing unit
- DCNN:
-
Deep convolutional neural network
- DNN:
-
Deep neural network
- DPC:
-
Decision pyramid classifier
- EK-LFH:
-
ETRI-KAIST labeled faces in the heterogeneous environment
- FLD:
-
Fisher linear discriminant
- FR:
-
Face recognition
- FV:
-
Face verification
- GAN:
-
Generative adversarial network
- GFN:
-
Gabor feedforward network
- GPU:
-
Graphics processing unit
- KLD:
-
Kullback–Leibler divergence
- LDA:
-
Linear discriminant analysis
- LDP:
-
Locality dispersion projection
- LFW:
-
Labelled faces in the wild
- NLP:
-
Natural language processing
- ORL:
-
Olivetti research laboratory
- OSPP:
-
One sample per person
- PCA:
-
Principal component analysis
- ReLU:
-
Rectified linear unit
- RNN:
-
Recurrent neural network
- SID:
-
Single image deraining
- SIFT:
-
Scale-invariant feature transform
- SOD:
-
Salient object detection
- SOM:
-
Self organizing map
- SRC:
-
Sparse representation classifier
- SSFR:
-
Single sample face recognition
- SSFRDL:
-
Single sample face recognition using deep learning
- SVD:
-
Singular value decomposition
- SVM:
-
Support vector machine
- UFI:
-
Unconstrained facial images
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Tomar, V., Kumar, N. & Srivastava, A.R. Single sample face recognition using deep learning: a survey. Artif Intell Rev 56 (Suppl 1), 1063–1111 (2023). https://doi.org/10.1007/s10462-023-10551-y
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DOI: https://doi.org/10.1007/s10462-023-10551-y