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Age-invariant face recognition based on deep features analysis

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Abstract

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.

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Authors and Affiliations

  1. Electronics and Communication Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt

    Amal A. Moustafa, Ahmed Elnakib & Nihal F. F. Areed

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  1. Amal A. Moustafa
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  2. Ahmed Elnakib
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  3. Nihal F. F. Areed
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Correspondence to Ahmed Elnakib.

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Moustafa, A.A., Elnakib, A. & Areed, N.F.F. Age-invariant face recognition based on deep features analysis. SIViP 14, 1027–1034 (2020). https://doi.org/10.1007/s11760-020-01635-1

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  • DOI: https://doi.org/10.1007/s11760-020-01635-1

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