Joint Face Retrieval System Based On a New Quadruplet Network in Videos of Multi-Camera

At present, a large number of off-line videos is stored in the server of surveillance network. In order to retrieve the target face in these massive videos frames, the face retrieval system is designed. A new Quadruplet Network is constructed by changing the RELU structure of CNN network and training the new Quadruplet Network to acquire the depth features. Join with the online fugitive face picture that launched online to initiate the wanted, with the help of the depth feature contrast to launch the Content-Based Image Retrieval (CBIR). The new Quadruplet Network converges faster than familiar networks such as Alexnet, Googlenet, VGGNet and ResNet. Because of the shared weight design of the network, the retrieval has a high precision, recall and the retrieval rate. Image depth features can be shared quickly online between the cameras. The experimental results show that the proposed method is effective, with an accuracy of 98.74% and a precision of 99.54%, and a frame rate of 28 FPS.