A Cross Benchmark Assessment of a Deep Convolutional Neural Network for Face Recognition

Deep convolutional neural networks (DCNN) based algorithm methods have swept face-recognition. DCNNbased algorithms have shown significant improvements in accuracy on the Labeled Faces in the Wild (LFW) and the YouTube1 Video face-recognition benchmarks. These two benchmarks consist of images and videos of celebrities downloaded from the World Wide Web. Since 2004, the National Institute of Standards and Technology (NIST) has established a series of face-recognition benchmarks that span a range of scenarios and difficulties. The scenarios range from comparing frontal faces taken in studio lighting to comparing faces acquired with cell phone cameras taken outdoors. The VGG-face algorithm [7] was ran on eight NIST face-recognition benchmarks. The Vision Geometry Group (VGG)-face algorithm excelled on the most difficult benchmarks; existing algorithms excelled the benchmarks with higher quality images. This finding is consistent with the design of the algorithms. The VGG-face algorithm was designed to recognize faces in variable illumination; the existing algorithms were designed to operate on face-images taken in controlled illuminations. To accurately characterize the performance of face recognition algorithms, we recommend that performance is reported on multiple benchmarks.