| dc.description.abstract | In recent years, thanks to the development of CNN (convolutional neural network), researchers have made great progress on face detection and face recognition. Many unique and novel network structures have been proposed to solve different face detection or recognition problems. To use which network structure depends on the application, for example, we only need to perform face recognition on an image with only one face at customs. However, in monitoring or access control system, we need to perform face detection first to find where faces are and then recognize every faces.
We propose a CNN structure which combines face detection and face recognition. We use the RPN structure from Faster R-CNN to propose candidate regions which may be faces. We then use a coarse-to-fine cascaded CNN to check each candidate regions and filter out the regions which are not faces. By using RPN structure instead of using sliding widow to propose candidate region, we can avoid checking regions in every sizes and at every places one by one. The system needs only 0.08 seconds with RPN structure, compared to 0.18 seconds with sliding window method, we get better execution speed, and the detection capability remains nearly the same.
After finishing face detection, we then use FaceNet to extract features for recognition. Due to the definition of the loss function, the distance between two feature vectors extracted from two facial images can reflect the similarity of the two facial images. That is, we can recognize faces by only calculate the distance between feature vectors without using any complex classifiers, which allows us to use the same recognition system in different situations. The recognition accuracy of the proposed method can reach 97%, which is slightly lower than the methods that need to be retrained. However, considering the convenience of using the same recognition system without retraining, we think it’s still a great deal. | en_US |