The human emotion recognition based on facial expression has a significant meaning in the application of intelligent man–machine interaction. However, the human face images vary largely in real environments due to the complex backgrounds and luminance. To solve this problem, this paper proposes a robust face detection method based on skin color enhancement model and a facial expression recognition algorithm with block principal component analysis (PCA). First, the luminance range of human face image is broadened and the contrast ratio of skin color is strengthened by the homomorphic filter. Second, the skin color enhancement model is established using YCbCr color space components to locate the face area. Third, the feature based on differential horizontal integral projection is extracted from the face. Finally, the block PCA with deep neural network is used to accomplish the facial expression recognition. The experimental results indicate that in the case of weaker illumination and more complicated backgrounds, both the face detection and facial expression recognition can be achieved effectively by the proposed algorithm, meanwhile the mean recognition rate obtained by the facial expression recognition method is improved by 2.7% comparing with the traditional Local Binary Patterns (LBPs) method.

中文翻译：

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复杂背景下基于深度信念网络的人脸和面部表情检测的人类情感识别

基于面部表情的人类情感识别在智能人机交互的应用中具有重要意义。然而，由于复杂的背景和亮度，人脸图像在真实环境中变化很大。针对这一问题，本文提出了一种基于肤色增强模型的鲁棒人脸检测方法和一种基于块主成分分析（PCA）的人脸表情识别算法。首先，通过同态滤波器拓宽人脸图像的亮度范围，增强肤色对比度。其次，建立肤色增强模型，利用YCbCr颜色空间分量对人脸区域进行定位。第三，从人脸中提取基于微分水平积分投影的特征。最后，具有深度神经网络的块PCA用于完成面部表情识别。实验结果表明，在光照较弱、背景较复杂的情况下，该算法能够有效实现人脸检测和人脸表情识别，同时人脸表情识别方法的平均识别率提高了2.7%。与传统的局部二进制模式 (LBP) 方法相比。