现代信息科技2026，Vol.10Issue(10)：76-82,88,8.DOI:10.19850/j.cnki.2096-4706.2026.10.014
基于LoRA微调与小波注意力增强的CLIP表情识别方法
CLIP Expression Recognition Method Based on LoRA Fine-tuning and Wavelet Attention Enhancement
摘要
Abstract
In facial expression recognition tasks,the scarcity of high-quality annotated data—particularly for minority categories such as disgust and fear—poses a significant constraint on model performance.To address the data insufficiency of specific expression categories,this paper proposes a facial expression recognition method based on the CLIP vision large model,aiming to enhance the representational capacity for scarce expressions via a parameter-efficient adaptation mechanism.Specifically,an adaptive adjustment strategy is introduced for the CLIP visual encoder,with the base model parameters kept frozen during LoRA fine-tuning to achieve effective alignment between pre-trained visual representations and the facial expression recognition task.Furthermore,this paper presents a wavelet channel attention enhancement module,which suppresses high-frequency random noise in features through multi-scale decomposition and channel-adaptive weighting mechanisms,thereby strengthening the capture of subtle expression dynamics.Finally,a gated fusion module is proposed,which employs a learnable channel-level weight allocation mechanism to achieve effective complementarity between local detail features and high-level semantic features.Extensive experimental results demonstrate that the proposed method achieves high recognition accuracy,validating the effectiveness and robustness in expression recognition tasks.关键词
面部表情识别/预训练/小波变换/特征提取Key words
facial expression recognition/pre-training/wavelet transform/feature extraction分类
信息技术与安全科学引用本文复制引用
吴晨倩,朱恒亮..基于LoRA微调与小波注意力增强的CLIP表情识别方法[J].现代信息科技,2026,10(10):76-82,88,8.基金项目
福建省自然科学(2023J01348) （2023J01348）
