小数据样本深度迁移网络自发表情分类  

Classification of small spontaneous expression database based on deep transfer learning network

作　　者：付晓峰[1] 吴俊[1] 牛力 Fu Xiaofeng;Wu Jun;Niu Li(School of Computer Science and Technology,Hangzhou Dianzi Universty,Hangzhou 310018. China)

机构地区：[1]杭州电子科技大学计算机学院,杭州310018

出　　处：《中国图象图形学报》2019年第5期753-761,共9页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61672199;61572161);浙江省科技计划项目--2018年度重点研发计划项目(2018C01030);浙江省自然科学基金项目(Y1110232)~~

摘　　要：目的相较于传统表情,自发表情更能揭示一个人的真实情感,在国家安防、医疗等领域有巨大的应用潜力。由于自发表情具有诱导困难、样本难以采集等特殊性,因此数据样本较少。为判别自发表情的种类,结合在越来越多的场景得到广泛应用的神经网络学习方法,提出基于深度迁移网络的表情种类判别方法。方法为保留原始自发表情图片的特征,即使在小数据样本上也不使用数据增强技术,并将光流特征3维图像作为对比样本。将样本置入不同的迁移网络模型中进行训练,然后将经过训练的同结构的网络组合成同构网络并输出结果,从而实现自发表情种类的判别。结果实验结果表明本文方法在不同数据库上均表现出优异的自发表情分类判别特性。在开放的自发表情数据库CASME、CASMEⅡ和CAS(ME)~2上的测试平均准确率分别达到了94. 3%、97. 3%和97. 2%,比目前最好测试结果高7%。结论本文将迁移学习方法应用于自发表情种类的判别,并对不同网络模型以及不同种类的样本进行比较,取得了目前最优的自发表情种类判别的平均准确率。Objective Expression is important in human-computer interaction.As a special expression,spontaneous expression features shorter duration and weaker intensity in comparison with traditional expressions.Spontaneous expressions can reveal a person’s true emotions and present immense potential in detection,anti-detection,and medical diagnosis.Therefore,identifying the categories of spontaneous expression can make human-computer interaction smooth and fundamentally change the relationship between people and computers.Given that spontaneous expressions are difficult to be induced and collected,the scale of a spontaneous expression dataset is relatively small for training a new deep neural network.Only ten thousand spontaneous samples are present in each database.The convolutional neural network shows excellent performance and is thus widely used in a large number of scenes.For instance,the approach is better than the traditional feature extraction method in the aspect of improving the accuracy of discriminating the categories of spontaneous expression.Method This study proposes a method on the basis of different deep transfer network models for discriminating the categories of spontaneous expression.To preserve the characteristics of the original spontaneous expression,we do not use the technique of data enhancement to reduce the risk of convergence.At the same time,training samples,which comprise three-dimensional images that are composed of optical flow and grayscale images,are compared with the original RGB images.The threedimensional image contains spatial information and temporal displacement information.In this study,we compare three network models with different samples.The first model is based on Alexnet that only changes the number of output layer neurons that is equal to the number of categories of spontaneous expression.Then,the network is fine-tuned to obtain the best training and testing results by fixing the parameters of different layers several times.The second model is based on Inception V3.Two fully

关 键 词：自发表情 迁移学习 分类 神经网络 同构网络 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]