Face Recognition Based on MTCNN and Convolutional Neural Network

MTCNN is a face detection method based on deep learning, which is more robust to light, angle and facial expression changes in natural environment, and has better face detection effect. At the same time, the memory consumption is small, and real-time face detection can be realized. Therefore, a method based on MTCNN and improved convolution neural network is proposed in this paper. Firstly, MTCNN is used to detect and align faces. Then, the output image is used as the input data of the improved convolution network, and multi-level convolution training is carried out. Finally, the accuracy of the model is tested.

[1]  Jian Sun,et al.  Blessing of Dimensionality: High-Dimensional Feature and Its Efficient Compression for Face Verification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Shuzhi Sam Ge,et al.  Learning Saliency Features for Face Detection and Recognition Using Multi-task Network , 2016, International Journal of Social Robotics.

[3]  Yann LeCun,et al.  Learning a similarity metric discriminatively, with application to face verification , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[4]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Ah Chung Tsoi,et al.  Face recognition: a convolutional neural-network approach , 1997, IEEE Trans. Neural Networks.

[7]  Yu Qiao,et al.  Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks , 2016, IEEE Signal Processing Letters.

[8]  Shiguang Shan,et al.  Fusing Robust Face Region Descriptors via Multiple Metric Learning for Face Recognition in the Wild , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[10]  Oren Barkan,et al.  Fast High Dimensional Vector Multiplication Face Recognition , 2013, 2013 IEEE International Conference on Computer Vision.

[11]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[12]  Ting Liu,et al.  Recent advances in convolutional neural networks , 2015, Pattern Recognit..

[13]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[14]  Jian Sun,et al.  A Practical Transfer Learning Algorithm for Face Verification , 2013, 2013 IEEE International Conference on Computer Vision.

[15]  Jian Sun,et al.  Bayesian Face Revisited: A Joint Formulation , 2012, ECCV.