Research On Bi-mode Biometrics Based On Deep Learning

In view of the fact that biological characteristics have excellent independent distinguishing characteristics,Biometric identification technology involves almost all the relevant areas of human distinction.Fingerprints,iris,face,voiceprint and other biological features have been widely used in the public security departments to detect detection,mobile equipment unlock,target tracking and other fields.With the use of electronic devices more and more widely and the frequency is getting higher and higher.Only the Biometric identification technology with excellent recognition rate can guarantee the long-term development of these fields.Due to differences in environmental conditions using biometrics recognition in real life.Single biometrics because of its own limitations,lead to any kind of biometrics there will be limitations of its application.For example,the premise of the use of fingerprint identification equipment is the target people must have physical contact,video surveillance and other remote devices can not use fingerprint recognition technology;The recognition rate of face recognition technology will drop sharply if there is not enough light or if the camera is not facing the target person's face;Similarly,the iris recognition technology must ensure that the target person's eye close to the sensor in order to achieve this biological characteristics of the follow-up process.Multi-biometric fusion identification technology can be a good solution to this problem.Multi-modal biometric fusion recognition technology can improve the accuracy,universality and robustness of the identification device according to the feature selection and fusion method.The main contents and experimental results of this paper are as follows:1.Based on the Convolutional Neural Network(CNN)and the face recognition,a new face recognition algorithm based on Vgg Face improved model is proposed.This method combines the core idea of the deep convolution network model.In the last layer of the Vgg_Face model,the full join layer is added to reduce the face feature dimension.Fine-turning a new model based on the Vgg_Face model.Use the CASIA-Webface face database to train the network model.2.Firstly,the Perceptual Linear Predictive(PLP)of the speaker is studied,and then the speaker's I-Vector feature extraction method is deduced.The I-Vector feature reflects the speaker's differences and has excellent cross-channel performance.It's the mainstream identification characteristics of Speaker identify.And then sent the I-Vector and PLP features into the deep confidence network(Deep Belief Network,DBN)to training speaker recognition model.3 The fusion method of face model and speaker character fusion is put forward.Combined with TED-LIUM speech database and CASIA-WebFace face database,different face and voice randomly will be combined into a new face-speaker integrated library.The dataset is extracted from the fusion feature to train the DBN.The resulting model identifies the target person whose recognition rate is higher than the recognition rate of the individual face feature or speaker feature alone.