Abstract
Face recognition is a hot topic in the field of artificial intelligence in recent years. In many practical applications, face data acquisition is difficult, the accuracy of face recognition based on small samples can’t reach a satisfactory accuracy. To solve this problem, a lightweight convolutional neural network (LWCNN) base on deep learning is built for small sample data. In LWCNN, two convolutional blocks are used to extract the features of the input face image. Then the k-fold cross validation is adopted to verify the robustness of the network. The simulation results show that compared with other three methods, our proposed LWCNN achieves better recognition accuracy in small sample space and avoids the over-fitting problem to a certain extent.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
