其他知识库

人脸表情识别在众多场景下都有应用， 但是由于光照变化、面部外观改变和遮挡的影响， 导致识别准确率下降. 针对此现象， 提出将改进的Island损失函数用于辅助表情识别. Island损失函数虽然能够提高测试性能， 但是却降低了泛化性能， 针对这一问题， 改进的Island损失函数采用指数损失减小类内距离的方法提高泛化性能， 采用余弦距离损失与欧几里得度量损失增大类间距离的方法进一步提高测试性能. 结果证明， 与Island损失函数相比， LCE损失函数与ECE损失函数均可以同时提高测试性能与泛化性能， ECE损失函数在CK+， JAFFE， SFEW， RAF和Older数据集上的平均泛化性能提高了1.05%.

Facial expression recognition is widely used in many scenes, but the recognition accuracy is reduced due to the influence of illumination, facial appearance and occlusion. In view of the phenomenon, an improved Island loss was proposed to assist facial expression recognition. Although Island loss can improve test performance, it reduces generalization performance. To solve this problem, the improved Island loss used exponential loss to reduce intra-class distance to improve the generalization performance, and used cosine distance loss and Euclidean distance loss to increase inter-class distance to further improve the test performance. The experimental results indicate that LCE loss and ECE loss can both improve the test performance and generalization performance at the same time compared with Island loss. The average generalization performance of ECE loss on CK+, JAFFE, SFEW, RAF and Older datasets increases by 1.05%.