摘要
目前,在普通手机解锁、面部锁定、面部扫描支付和安全防卫等领域,人脸识别信息技术都有着非常广泛的应用。传统的机器学习算法是基于人的脸部特征的,由于每类样本的不均衡性严重和场景效果的欠缺等因素,算法实现的效果常常不是很理想。本文将针对目前人脸识别技术领域的高语义特征及深度特征提取的缺陷,以及人工提取存在的误差,运用深度学习算法和类比中心等算法,提高人脸识别在特征提取领域的技术能力。利用卷积神经网络减少人工干预,提高特征提取的算法精度,从而提高损失函数的精确值。
Face recognition technology is widely used in ordinary mobile phone unlocking,face locking,face scanning payment,and security protection.Traditional machine learning algorithms are based on human facial features,and the effect is often not ideal due to serious imbalance of each type of sample and lack of scene effect.This paper aims to use deep learning algorithm and analog center algorithm to improve technical capability of face recognition with respect to feature extraction.Thus,defects of high semantic features and deep feature extraction,as well as errors in manual extraction will be greatly reduced.Convolutional neural network is used to reduce manual intervention,improve accuracy of feature extraction algorithm,so to improve the performance of loss function.
作者
巩震
陈丹红
GONG Zhen;CHEN Danhong(Shenyang Aerospace University,Shenyang 110136,China)
出处
《软件工程》
2021年第1期10-12,共3页
Software Engineering
基金
沈阳航空航天大学大学生创新创业训练计划项目(S202010143007).
关键词
人脸识别
算法优化
深度学习
卷积神经网络
face recognition
algorithm optimization
deep learning
convolutional neural network
