摘要
针对因光照、拍摄角度及图片质量等因素导致的经典深度学习算法难以有效提取人脸特征、人物身份识别准确率难以达到理想精度的问题,提出一种基于人脸强语义的年龄识别算法.首先,通过注意力矩阵增强人脸区域的特征权重,达到提取特征区域的目的;其次,使用级联双向长短期记忆(Bi-LSTM)网络学习时序帧之间的特征依赖关系,弥补部分特征缺失对识别精度的影响.在人脸数据集IMDB-WIKI和数据集Adience上进行测试,该算法的年龄识别准确率分别达到78.34%和77.89%.实验结果表明,相比于其他基于深度学习算法的方法,该算法在基于图片数据集的人物年龄识别任务上具有更高的准确率.
Aiming at the problems that the classical deep learning algorithm was difficult to extract facial features effectively and the accuracy of character identification was difficult to reach the ideal accuracy due to factors such as illumination,shooting angle and image quality,we proposed an age recognition algorithm based on strong facial semantics.Firstly,the feature weights of facial regions were enhanced by the attention matrix to achieve the purpose of extracting feature regions.Secondly,a cascaded bi-directional long short-term memory(Bi-LSTM)network was used to learn the feature dependency relationships between temporal frames and compensate for the influence of missing features on recognition accuracy.When tested on IMDB-WIKI facial dataset and Adience dataset,the age recognition accuracy of the algorithm reached 78.34%and 77.89%,respectively.Experimental results show that compared with other methods based on deep learning algorithms,the proposed algorithm has higher accuracy in the task of person age recognition based on image datasets.
作者
孙旭菲
缪新颖
毕甜甜
王水涛
喻芳宇
SUN Xufei;MIAO Xinying;BI Tiantian;WANG Shuitao;YU Fangyu(College of Information Engineering,Dalian Ocean University,Dalian 116023,Liaoning Province,China;Key Laboratory of Liaoning Provincial Marine Information Technology,Dalian 116023,Liaoning Province,China)
出处
《吉林大学学报（理学版）》
CAS
北大核心
2024年第2期347-356,共10页
Journal of Jilin University:Science Edition
基金
辽宁省揭榜挂帅重点项目(批准号:2022081)
设施渔业教育部重点实验室开放课题项目(批准号:202306)。
