基于增强卷积神经网络的跨年龄人脸验证

A. M. Osman, Serestina Viriri
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引用次数: 0

摘要

提出了一种基于深度学习的人脸识别方法。面部老化是一种随着时间的推移而影响人脸的纹理和形状变化。因此,有必要开发强大的方法来验证面部图像时,他们的年龄。本文将基于GoogLeNet预训练卷积网络的深度学习方法与直方图方向梯度(Histogram Orientation Gradient, HOG)和局部二值模式(Local Binary Pattern, LBP)特征描述子相融合,用于特征提取和分类。实验基于MORPH和FG-Net基准数据集收集的面部图像。欧几里得距离被用来衡量年龄差距对特征向量之间的相似性。实验结果表明,FG-NET数据库的验证准确率达到100%,而MORPH数据库的验证准确率为99.8%。与现有方法相比,该方法具有更好的性能和更高的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Face Verification Across Age Progression using Enhanced Convolution Neural Network
This paper proposes a deep learning method for facial verification of aging subjects. Facial aging is a texture and shape variations that affect the human face as time progresses. Accordingly, there is a demand to develop robust methods to verify facial images when they age. In this paper, a deep learning method based on GoogLeNet pre-trained convolution network fused with Histogram Orientation Gradient (HOG) and Local Binary Pattern (LBP) feature descriptors have been applied for feature extraction and classification. The experiments are based on the facial images collected from MORPH and FG-Net benchmarked datasets. Euclidean distance has been used to measure the similarity between pairs of feature vectors with the age gap. Experiments results show an improvement in the validation accuracy conducted on the FG-NET database, which it reached 100%, while with MORPH database the validation accuracy is 99.8%. The proposed method has better performance and higher accuracy than current state-of-the-art methods.
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