The biometric information contained in a face image is affected by many factors such as living environment, racial differences, and genetic diversity, this complexity leads to the nonstationary of the age estimation. In order to reduce the overlap of face features between adjacent ages and improve the accuracy of age prediction, a multi-stage feature constraints learning method is proposed for face age estimation. The proposed method gradually refines the feature through three feature constraint stages. In each stage, the algorithm continuously updates the feature center of its corresponding age range, and minimizes the distance between each age feature and feature center of the corresponding age range through feature constraint. Feature constraint makes the feature distances between different individuals in the same age feature space smaller and decrease the overlap areas between adjacent age range feature spaces. Meanwhile, the feature distance of different age range feature space is enlarged. The proposed network efficiently merges the features of three stages and optimizes the mapping of feature maps to an ordered binary comparison space. Experiments show that the proposed method is able to effectively improve the discrimination between different age features, and hence to improve the accuracy of face age estimation. In addition, the proposed algorithm is simple enough to achieve fast face age estimation.

中文翻译：

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年龄估计的多阶段特征约束学习


人脸图像中包含的生物特征信息受到生活环境、种族差异、遗传多样性等多种因素的影响，这种复杂性导致年龄估计的非平稳性。为了减少相邻年龄之间人脸特征的重叠并提高年龄预测的准确性，提出了一种用于人脸年龄估计的多阶段特征约束学习方法。该方法通过三个特征约束阶段逐渐细化特征。在每个阶段，算法不断更新其对应年龄范围的特征中心，并通过特征约束最小化每个年龄特征与对应年龄范围的特征中心之间的距离。特征约束使得同一年龄特征空间中不同个体之间的特征距离更小，减少了相邻年龄范围特征空间之间的重叠面积。同时，扩大了不同年龄段特征空间的特征距离。所提出的网络有效地合并了三个阶段的特征，并优化了特征图到有序二进制比较空间的映射。实验表明，该方法能够有效提高不同年龄特征的区分度，从而提高人脸年龄估计的准确性。此外，所提出的算法足够简单，可以实现快速的人脸年龄估计。