基于二值化密集卷积神经网络的表情识别算法

Expression Recognition Algorithm Based on Binary Dense Convolution Neural Network

人脸表情识别已成为人工智能领域的重要研究课题,但传统的卷积神经网络需要庞大的计算资源使得其应用受限,而二值化卷积神经网络可通过快速与或运算代替原本的浮点乘法运算,大大降低了算法对计算资源的需求。论文提出了一种基于数据增强和二值化卷积神经网络的人脸表情识别算法,通过均值估计,在FER2013数据集上达到了66.15%的识别率,超越了部分基于浮点乘积运算的卷积网络,为表情识别算法移植到小型设备中提供了可能。

The research of facial expression recognition has become an important topic in the field of artificial intelligence.However,the requirement for huge computing resources has limited the application of traditional convolutional neural networks.Since the binary neural network replaces the floating point multiplication arithmetic by fast AND OR arithmetic,the need of computing resources can be greatly reduced. In this paper,a facial expression recognition algorithm based on data enhancement and binary convolutional neural network is proposed,and 66.15% expression recognition accuracy is obtained on the dataset FER2013. The algorithm has surpassed some convolutional neural network algorithms based on floating point multiplication arithmetic,which makes it possible to transplant expression recognition algorithms into small devices.