论文标题
评估面部情感识别的自学成才的基于学习的表述
Evaluation of Self-taught Learning-based Representations for Facial Emotion Recognition
论文作者
论文摘要
这项工作描述了不同的策略，以产生通过自学成才的面部情感识别概念获得的无监督表述（FER）。这个想法是通过改变自动编码器的初始化，体系结构和培训数据来创建促进多样性的互补表示。将SVM，包装，随机森林和动态集合选择方法评估为最终分类方法。使用一项受试者的协议对Jaffe和Cohn-Kanade数据集进行的实验结果表明，基于提议的不同表示形式的FER方法与最新的方法相比，该方法与最新的方法相比，这些方法也探索了无处可比的特征学习。
This work describes different strategies to generate unsupervised representations obtained through the concept of self-taught learning for facial emotion recognition (FER). The idea is to create complementary representations promoting diversity by varying the autoencoders' initialization, architecture, and training data. SVM, Bagging, Random Forest, and a dynamic ensemble selection method are evaluated as final classification methods. Experimental results on Jaffe and Cohn-Kanade datasets using a leave-one-subject-out protocol show that FER methods based on the proposed diverse representations compare favorably against state-of-the-art approaches that also explore unsupervised feature learning.
