Age-invariant face recognition based on deep features analysis

  • Amal A. Moustafa
  • Ahmed ElnakibEmail author
  • Nihal F. F. Areed
Original Paper


Age-invariant face recognition is one of the most crucial computer vision problems, e.g., in passport verification, surveillance systems, and missing individuals identification. The extraction of robust face features is a challenge since the facial characteristics change over age progression. In this paper, an age-invariant face recognition system is proposed, which includes four stages: preprocessing, feature extraction, feature fusion, and classification. Preprocessing stage detects faces using Viola–Jones algorithm and frontal face alignment. Feature extraction is achieved using a CNN architecture using VGG-Face model to extract compact face features. Extracted features are fused using the real-time feature-level multi-discriminant correlation analysis, which significantly reduces feature dimensions and results in the most relevant features to age-invariant face recognition. Finally, K-nearest neighbor and support vector machine are investigated for classification. Our experiments are performed on two standard face-aging datasets, namely FGNET and MORPH. Rank-1 recognition accuracy of the proposed system is 81.5% on FGNET and 96.5% on MORPH. Experimental results outperform the current state-of-the-art techniques on same data. These preliminary results show the promise of the proposed system for personal identification despite aging process.


Face recognition Aging Convolutional neural networks (CNN) Transfer learning Feature fusion Multi-discriminant correlation analysis (MDCA) 



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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2020

Authors and Affiliations

  1. 1.Electronics and Communication Engineering Department, Faculty of EngineeringMansoura UniversityMansouraEgypt

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