Pooling Faces: Template based Face Recognition with Pooled Face Images

TL;DR

This paper introduces a novel face recognition method using pooled face images to improve accuracy and reduce computational costs, demonstrating competitive results on standard benchmarks.

Contribution

It explores the use of average pooling of face images based on quality and pose, a novel application in face recognition, achieving state-of-the-art performance.

Findings

Outperforms published state-of-the-art methods

Requires fewer cross-template comparisons

Image pooling performs on par with deep feature pooling

Abstract

We propose a novel approach to template based face recognition. Our dual goal is to both increase recognition accuracy and reduce the computational and storage costs of template matching. To do this, we leverage on an approach which was proven effective in many other domains, but, to our knowledge, never fully explored for face images: average pooling of face photos. We show how (and why!) the space of a template's images can be partitioned and then pooled based on image quality and head pose and the effect this has on accuracy and template size. We perform extensive tests on the IJB-A and Janus CS2 template based face identification and verification benchmarks. These show that not only does our approach outperform published state of the art despite requiring far fewer cross template comparisons, but also, surprisingly, that image pooling performs on par with deep feature pooling.