一种基于深度学习的改进人脸识别算法

针对当前许多算法在非约束条件下特征判别能力不强、人脸识别性能不佳等问题，提出一种基于深度学习的改进人脸识别算法，通过训练多任务级联卷积神经网络，完成非约束图像的人脸检测和人脸归一化，提高训练图像的人脸信息，减少对模型的干扰。同时使用Softmax损失与中心损失联合监督训练模型，优化类内聚合、类间分散。实验结果表明，该算法提高了模型的特征判别能力，在LFW标准测试集上达到了较高的识别率。

Improved Face Recognition Method Based on Deep Learning

Aiming at the problems of low non-constrained feature discriminative ability and poor face recognition performance of current many algorithms, an improved face recognition algorithm based on deep learning is proposed. By training multi-task cascading convolutional neural networks, face detection and face normalization for unconstrained training face images are accomplished, which improves the face information of the training image and reduces the interference to the model. At the same time, the model is jointly supervised and trained by using Softmax Loss and Central Loss to compact intra-class and to disperse inter-class. The experimental results show that the algorithm improves the feature discriminant ability of the model and achieves higher recognition rate on the LFW standard test set.