基于改进高分辨率网络的人脸性别和年龄识别

针对目前利用人脸特征进行性别和年龄识别率较低的问题, 提出一种基于改进高分辨率网络(improved high-resoultion net, IHRNet)的新方法. 首先, 在IHRNet中融合具有少量参数和较高识别率的MobileNetV3结构, 结合高分辨率网络自身具有的多尺度特征提取优势, 有效提升了人脸特征识别的准确率; 其次, 为降低过拟合风险, 网络先采用IMDB-WIKI人脸数据集进行预训练, 然后加载预训练模型在Adience人脸数据集中进行训练和测试； 最后, 与ResNet50,HRNet,MobileNetV3三种同类算法进行对比. 实验结果表明, IHRNet在年龄及性别识别上的准确率分别高达82%,95%, 比同类算法分别平均提升9%和3%, 且参数量较未改进时下降36%, 验证了改进算法的有效性.

Facial Gender and Age Recognition Based on Improved High-Resolution Network

Aiming at the problem that the recognition rate of gender and age using facial features was low, we proposed a new method based on improved high-resoultion network (IHRNet). Firstly, we integrated the MobileNetV3 structure with a small number of parameters and a high recognition rate in IHRNet, and combined with the advantages of multi-scale feature extraction of the high-resolution network, which effectively improved the accuracy of facial feature recognition. Secondly, in order to reduce the risk of overfitting, the network first used the IMDB-WIKI face data set for pre-training, and then loaded the pre-training model for training and testing on the Adience face data set. Finally, compared with three similar algorithms such as ResNet50, HRNet and MobileNetV3. The experimental results show that the accuracy of IHRNet in age and gender recognition is as high as 82% and 95%, respectively, which are an average of 9% and 3% higher than similar algorithms, respectively, and the number of parameters is reduced by 36% compared with the previous one. The effectiveness of the improved algorithm is verified.