VLAD encoded Deep Convolutional features for unconstrained face verification

Abstract

We present a method for combining the Vector of Locally Aggregated Descriptor (VLAD) feature encoding with Deep Convolutional Neural Network (DCNN) features for unconstrained face verification. One of the key features of our method, called the VLAD-encoded DCNN (VLAD-DCNN) features, is that spatial and appearance information are simultaneously processed to learn an improved discriminative representation. Evaluations on the challenging IARPA Janus Benchmark A (IJB-A) face dataset show that the proposed VLAD-DCNN method is able to capture the salient local features and yield promising results for face verification. Furthermore, we show that additional performance gains can be achieved by simply fusing the VLAD-DCNN features that capture the local variations with the traditional DCNN features which characterize more global features.