Compressing Fisher Vector for Robust Face Recognition

One major topic for robust face recognition could be the efficient encoding of facial descriptors. Among various encoders, Fisher vector (FV) is one of the probabilistic methods that yield promising results. However, its huge representation is fairly forbidding. In this paper, we present approaches to efficiently compress FV and retain its robustness. First, we put forward a new Compact FV (CFV) descriptor. The CFV is obtained by zeroing out small posteriors, calculating first-order statistics and reweighting its elements properly. Second, in light of Iterative Quantization (ITQ) scheme, we present a Generalized ITQ (GITQ) method to binarize our CFV. Finally, we apply our CFV and GITQ to encode convolutional activations of convolutional neural networks. We evaluate our methods on FERET, LFW, AR, and FRGC 2.0 datasets, and our experiments reveal the advantage of such a framework.