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.

中文翻译：

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基于深度特征分析的年龄不变人脸识别

年龄不变的人脸识别是最关键的计算机视觉问题之一，例如在护照验证、监控系统和失踪人员身份识别中。由于面部特征随着年龄的增长而变化，因此提取鲁棒的面部特征是一项挑战。本文提出了一种年龄不变的人脸识别系统，包括预处理、特征提取、特征融合和分类四个阶段。预处理阶段使用 Viola–Jones 算法和正面人脸对齐来检测人脸。特征提取是使用 CNN 架构实现的，使用 VGG-Face 模型提取紧凑的人脸特征。使用实时特征级多判别相关分析融合提取的特征，这显着减少了特征维度，并产生了与年龄不变的人脸识别最相关的特征。最后，研究了K-最近邻和支持向量机进行分类。我们的实验是在两个标准的人脸老化数据集上进行的，即 FGNET 和 MORPH。所提出系统的 Rank-1 识别准确率在 FGNET 上为 81.5%，在 MORPH 上为 96.5%。实验结果在相同数据上优于当前最先进的技术。这些初步结果表明，尽管存在老化过程，但所提出的个人识别系统仍具有前景。所提出系统的 Rank-1 识别准确率在 FGNET 上为 81.5%，在 MORPH 上为 96.5%。实验结果在相同数据上优于当前最先进的技术。这些初步结果表明，尽管存在老化过程，但所提出的个人识别系统仍具有前景。所提出系统的 Rank-1 识别准确率在 FGNET 上为 81.5%，在 MORPH 上为 96.5%。实验结果在相同数据上优于当前最先进的技术。这些初步结果表明，尽管存在老化过程，但所提出的个人识别系统仍具有前景。