One to many face recognition with bilinear CNNs
The recent explosive growth in convolutional neural network (CNN) research has produced a variety of new architectures for deep learning. One intriguing new architecture is the bilinear CNN (B CNN) which has shown dramatic performance gains on certain fine grained recognition problems [15] We apply this new CNN to the challenging new face recognition benchmark, the IARPA Janus Benchmark A (IJB A) [12] It features faces from a large number of identities in challenging real world conditions. Because the face images were not identified automatically using a computerized face detection system, it does not have the bias inherent in such a database. We demonstrate the performance of the B CNN model beginning from an AlexNet style network pre trained on ImageNet. We then show results for fine tuning using a moderate sized and public external database, FaceScrub [17] We also present results with additional fine tuning on the limited training data provided by the protocol. In each case, the fine tuned bilinear model shows substantial improvements over the standard CNN. Finally, we demonstrate how a standard CNN pre trained on a large face database, the recently released VGG Face model [20] can be converted into a B CNN without any additional feature training. This B CNN improves upon the CNN performance on the IJB A benchmark, achieving 89.5% rank 1 recall.
2016 IEEE Winter Conference on Applications of Computer Vision (WACV)