HUANG Hao1，2， GE Hongwei1,2
（1. School of Artificial Intelligence and Computer Science， Jiangnan University， Wuxi 214122， China； 2. Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence， Wuxi 214122， China）
Abstract： Ambiguous expression is a common phenomenon in facial expression recognition (FER). Because of the existence of ambiguous expression, the effect of FER is severely limited. The reason maybe that the single label of the data cannot effectively describe complex emotional intentions which are vital in FER. Label distribution learning contains more information and is a possible way to solve this problem. To apply label distribution learning on FER, a label distribution expression recognition algorithm based on asymptotic truth value is proposed. Under the premise of not incorporating extraneous quantitative information, the original information of database is fully used to complete the generation and utilization of label distribution. Firstly, in training part, single label learning is used to collect the mean value of the overall distribution of data. Then, the true value of data label is approached gradually on the granularity of data batch. Finally, the whole network model is retrained using the generated label distribution data. Experimental results show that this method can improve the accuracy of the network model obviously, and has certain competitiveness compared with the advanced algorithms.
Key words： facial expression recognition (FER)； label distributed learning; label smoothing; ambiguous expression
References
［1］Deng J, Pang G, Zhang Z, et al. GAN based facial expression recognition for human-robot interaction. IEEE Access, 2019, 7: 9848-9859.
［2］Khan S A, Hussain S, Sun X M, et al. An effective framework for driver fatigue recognition based on intelligent facial expressions analysis. IEEE Access, 2018, 6: 67459-67468.
［3］Cui S, Song S, Si J, et al.. The influence of mouth opening and closing degrees on processing in NimStim facial expressions: An ERP study from Chinese college students. International Journal of Psychophysiology, 2021, 162: 157-165.
［4］Li S, Deng W H. Deep facial expression recognition: A survey. IEEE Transactions on Affective Computing, 2020: 1-20.
［5］Chen S, Wang J, Chen Y, et al. Label distribution learning on auxiliary label space graphs for facial expression recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR), Seattle: IEEE Computer Society, 2020: 13984-13993.
［6］Wang K, Peng X, Yang J, et al. Suppressing uncertainties for large-scale facial expression recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and PatternRecognition(CVPR), Seattle: IEEE Computer Society, 2020: 6897-6906.
［7］Jia X, Zheng X, Li W, et al. Facial emotion distribution learning by exploiting low-rank label correlations locally. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR), Long Beach: IEEE Computer Society, 2019: 9841-9850.
［8］Barsoum E, Zhang C, Ferrer C C, et al. Training deep networks for facial expression recognition with crowd-sourced label distribution.In: Proceedings of the 18th ACM International Conference on Multimodal Interaction(ICMI), Tokyo, 2016: 279-283.
［9］Geng X. Label distribution learning. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(7): 1734-1748.
［10］Gao B B, Xing C, Xie C W, et al. Deep label distribution learning with label ambiguity. IEEE Transactions on Image Processing, 2017, 26(6): 2825-2838.
［11］Zeng J, Shan S, Chen X. Facial expression recognition with inconsistently annotated datasets. In: Proceedings of the European Conference on Computer Vision (ECCV), Springer, 2018: 222-237.
［12］Li S, Deng W, Du J P. Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(ICCV), IEEE Computer Society Press, 2017: 2852-2861.
［13］Mollahosseini A, Hasani B, Mahoor M H. Affectnet: A database for facial expression, valence, and arousal computing in the wild. IEEE Transactions on Affective Computing, 2017, 10(1): 18-31.
［14］He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 2016: 770-778.
［15］Li Y, Zeng J, Shan S, et al. Occlusion aware facial expression recognition using CNN with attention mechanism. IEEE Transactions on Image Processing, 2018, 28(5): 2439-2450.
［16］Deng J, Pang G, Zhang Z, et al.cGAN based facial expression recognition for human-robot interaction. IEEE Access, 2019, 7: 9848-9859.
［17］Koujan M R, Alharbawee L, Giannakakis G, et al. Real-time Facial Expression Recognition “In The Wild”by Disentangling 3D Expression from Identity. In: Proceedings of 15th IEEE International Conference on Automatic Face and Gesture Recognition, 2020: 539-546.
［18］Li M, Xu H, Huang X, et al. Facial expression recognition with identity and emotion jointlearning. IEEE Transactions on Affective Computing, 2018, 14(8): 1-8.
［19］Miao S, Xu H, Han Z, et al. Recognizing facial expressions using a shallow convolutional neural network. IEEE Access, 2019, 7: 78000-78011.
［20］Siqueira H, Magg S, Wermter S. Efficient facial feature learning with wide ensemble-based convolutional neural networks. In: Proceedings of the 3rd Workshop on Affective Content Analysis (AffCon 2020). Menlo Park: AAAI, 2020, 34(4): 5800-5809.
渐近真值的标签分布表情识别算法
黄浩1,2， 葛洪伟1,2
（1. 江南大学 江苏省模式识别与计算智能实验室， 江苏 无锡 214122；2. 江南大学 人工智能与计算机学院， 江苏 无锡 214122）
摘要:歧义表情是表情识别中的常见现象， 由于歧义表情的存在， 表情识别的效果严重受限， 主要的原因是数据的单标签无法有效的描述其中复杂的感情倾向， 因而标签分布学习是解决该问题的一个可能的方向。 针对表情分类中单标签信息量不足的问题， 提出了一种渐近真值的标签分布表情识别算法。 在不引入额外信息的前提下， 充分利用数据库的原本信息完成标签分布的生成和利用。 首先， 在数据训练时， 利用单标签学习， 收集数据整体分布的均值； 然后， 在数据批次的粒度上， 逐步逼近数据标签真值； 最后， 利用生成的数据标签分布重新训练整个网络模型。 实验结果表明， 该方法对网络模型的精度提升有明显的作用， 在与先进算法的对比中也有一定的竞争力。
关键词:人脸表情识别； 标签分布式学习； 标签平滑； 歧义表情
引用格式：HUANG Hao， GE Hongwei. Label distribution expression recognition algorithm based on asymptotic truth value. Journal of Measurement Science and Instrumentation, 2021, 12（3）： 295-303. DOI： 10．3969／j．issn．1674-8042．2021．03．007
[full text view]
