基于多Inception结构的卷积神经网络人脸识别算法

Face Recognition Algorithms Based on Convolutional Neural Network with Multi-Inception Structure

人脸识别是视觉识别的一个重要领域,由于人脸识别尺度变化范围大,光照、姿态变化剧烈以及遮挡问题,导致该类非限制条件下的识别难度较大,为了解决该类问题,本文提出了一种基于Tensorflow平台的多Inception模型,通过将多个Inception结构进行串联,再通过分解卷积核的方式减少输入参数,实现了多维度同时卷积再聚合,提高了人脸识别的精度.实验结果表明,该方法在较少参数的条件下能提取出更具区分度的人脸特征,与分类损失方法及融合了其他度量学习方式的方法相比,提高了识别准确率,减少了计算时间.

Face recognition is an important field of visual recognition.Because of the large scale of variations in face recognition,namely drastic changes in illumination and pose,occlusion problems,and complex image background,it is difficult to recognize the face under such unrestricted conditions.In order to solve these problems,a multi-Inception model based on Tensorflow platform is proposed in this study.By combining multiple Inception knots,a multi-Inception-V3 model based on Tensorflow platform is proposed.The structure is connected in series,which realizes the convolution and re-aggregation of multiple dimensions at the same time,and improves the accuracy of face recognition.The experimental results show that the proposed method can extract more discriminant face features with fewer parameters.Compared with the classification loss method and the fusion of other metric learning methods,it improves the accuracy of face recognition under unconstrained conditions.