一种基于注意力模型的面部表情识别算法

An Attention Model-Based Facial Expression Recognition Algorithm

深度卷积网络提取的表情特征易受背景、个体身份等因素影响,其与无用特征混合在一起对表情识别造成干扰.针对此问题,提出一种基于注意力模型的面部表情识别算法,该方法基于一个轻量级的卷积神经网络以避免过拟合,通过通道注意力模块和空间注意力模块对特征图元素进行加强或抑制,应用残差学习单元使注意力模型学习到更丰富的特征并获得更好的梯度流.此外,还提出一种面部表情关键区域截取方案,以解决非表情区域的噪声干扰问题.在两个常用的表情数据集CK+和MMI上对所提方法进行了验证,实验结果证明了该方法的优越性.

Facial features extracted by a deep convolutional network are susceptible to background,individual identity,and other factors,which are mixed with unnecessary features that interfere with facial expression recognition.To solve this problem,an attention model-based facial expression recognition algorithm is proposed in this paper.To avoid overfitting,this method is based on a lightweight convolutional neural network.Moreover,the channel attention model and the spatial attention model are employed to strengthen or suppress the feature map elements.A residual learning unit is used to enable the attention model to learn rich features and obtain an excellent gradient flow.In addition,a key area crop scheme for facial expressions is proposed to solve the problem of noise interference in non-expressive regions.The proposed method is validated on two commonly used expression datasets:CK+and MMI.Experimental results demonstrate the superiority of the proposed method.