基于注意力机制和残差网络的手部热痕迹识别

Hand heat trace recognition based on attention mechanism and residual networks

手部热痕迹红外图像识别对刑事侦查具有重要意义,但热痕迹图像往往存在模糊问题。传统识别方法依靠人工设计特征,存在局限性,常规的深度学习方法对样本数量存在依赖性,均难以直接应用。利用卷积神经网络强大的特征表达能力,引入残差网络增强模型学习特征的性能,设计注意力机制模块从空间和通道维度提高模型对重要特征的关注度,最终构建了基于注意力机制的残差卷积神经网络。实验验证了该算法的有效性,取得了最高的识别准确率。

Infrared image recognition of hand thermal trace is of great significance to criminal investigation,but thermal trace images often have fuzzy problems.Traditional recognition methods rely on artificial design features,and conventional deep learning methods depend on the number of samples,so it is difficult to apply them directly.Using the strong feature expression ability of con-volutional neural network,the residual network is introduced to enhance the performance of learning features of the model,and the attention mechanism module is designed to improve the attention of the model to important features from the spatial and channel di-mensions.Finally,the residual convolutional neural network based on attention mechanism is constructed.Experimental results verify the effectiveness of the algorithm and achieve the highest recognition accuracy.