基于深度学习的人脸跟踪自动初始化方法

Automatic initialization of face tracking based on deep learning

针对机器学习领域的人脸跟踪研究,其人脸首帧初始化由人工手动标注的问题,提出了一种基于深度学习的人脸跟踪自动初始化首帧方法。通过建立栈式稀疏自编码神经网络,对大量未标注的样本采用近似恒等的方法计算各隐层节点并运用反向传播法进行权值微调。预训练网络之后,连接softmax分类器,再用少量已标注样本对softmax分类器进行有监督训练,从而形成一个能进行人脸跟踪首帧自动初始化的分类器。结果表明,该方法显著提高了人脸跟踪中首帧初始化的效率,识别准确率达到92%,基本满足了人脸首帧自动初始化的要求。

To overcome the problem that the face model is initialized by manual location in the first frame for face tracking, we propose an automatic initialization method based on deep learning. We establish a stack of sparse self-encoding with neural networks, use a large number of unlabeled samples to calculate the nodes of each hidden layer using the approximate identical method, and a back-propagation approach is used to fine-tune the weights. After pre-training the network that connects a softmax classifier, we do the supervised training using labeled samples. Thus, a classifier which can automatically initialize the first frame in face tracking is built up. The results show that the proposed method can significantly improve the efficiency of the first frame＇s initialization in face tracking, and the correct recognition rate can reach 92%, which basically achieves the automatic initialization of the first frame.