Application of Multiscale Facial Feature Manifold Learning Based on VGG-16
Application of Multiscale Facial Feature Manifold Learning Based on VGG-16
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DOI：
10.1155/2021/7129800
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Huilin Ge;Zhiyu Zhu;Runbang Liu;Xuedong Wu
中科院分区：
文献类型：
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作者：
Huilin Ge;Zhiyu Zhu;Runbang Liu;Xuedong Wu
目的。针对安防视频中人脸图像样本小、尺寸大、结构低、无标签、跟踪和重捕获困难等问题，提出了一种流行的基于VGG16的多尺度人脸特征流形算法。方法。首先建立VGG16框架，获取不同尺度上的人脸特征，并以不同尺度上的人脸特征为维度构建多尺度人脸特征流形。同时，使用识别率、准确率和运行时间来评估VGG16、LeNet-5和DenseNet在同一数据库上的性能。结果。从对比实验结果可以看出，VGG16的识别率和准确率是三个网络中最高的。VGG16的识别率为97.588%，准确率为95.889%。运行时间仅为3.5秒，比LeNet-5快72.727个百分点，比DenseNet快66.666个百分点。结论。该模型突破了公安领域人脸检测与跟踪问题中的关键问题，预测了人脸目标图像在时间维度流形空间中的位置，提高了人脸检测的效率。
Purpose. In order to solve the problems of small face image samples, high size, low structure, no label, and difficulty in tracking and recapture in security videos, we propose a popular multiscale facial feature manifold (MSFFM) algorithm based on VGG16. Method. We first build the VGG16 architecture to obtain face features at different scales and construct a multiscale face feature manifold with face features at different scales as dimensions. At the same time, the recognition rate, accuracy rate, and running time are used to evaluate the performance of VGG16, LeNet-5, and DenseNet on the same database. Results. From the results of comparative experiments, it can be seen that the recognition rate and accuracy of VGG16 are the highest among the three networks. The recognition rate of VGG16 is 97.588%, and the accuracy is 95.889%. And the running time is only 3.5 seconds, which is 72.727% faster than LeNet-5 and 66.666% faster than DenseNet. Conclusion. The model proposed in this paper breaks through the key problem in the face detection and tracking problem in the public security field, predicts the position of the face target image in the time dimension manifold space, and improves the efficiency of face detection.