| Title | Face Recognition Based on Densely Connected Convolutional Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Zhang, T., Wang, R., Ding, J., Li, X., Li, B. |
| Conference Name | 2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM) |
| Keywords | CAS-PEAL-Rl dataset, CASIA-WebFace dataset, compositionality, convolution, dense block layers, dense face model, densely connected convolutional networks, densely connected convolutional neural network, face recognition, face recognition methods, face recognition model, face verification accuracy, feature extraction, Information Reuse and Security, learning (artificial intelligence), LFW dataset, pattern classification, pubcrawl, recurrent neural nets, residual network, Resiliency |
| Abstract | The face recognition methods based on convolutional neural network have achieved great success. The existing model usually used the residual network as the core architecture. The residual network is good at reusing features, but it is difficult to explore new features. And the densely connected network can be used to explore new features. We proposed a face recognition model named Dense Face to explore the performance of densely connected network in face recognition. The model is based on densely connected convolutional neural network and composed of Dense Block layers, transition layers and classification layer. The model was trained with the joint supervision of center loss and softmax loss through feature normalization and enabled the convolutional neural network to learn more discriminative features. The Dense Face model was trained using the public available CASIA-WebFace dataset and was tested on the LFW and the CAS-PEAL-Rl datasets. Experimental results showed that the densely connected convolutional neural network has achieved higher face verification accuracy and has better robustness than other model such as VGG Face and ResNet model. |
| DOI | 10.1109/BigMM.2018.8499078 |
| Citation Key | zhang_face_2018 |
Face Recognition Based on Densely Connected Convolutional Networks