Sequential Person Recognition in Photo Albums with a Recurrent Network

TL;DR

This paper introduces a recurrent network model that jointly captures relational information, scene context, and appearance cues to improve person recognition in photo albums, achieving state-of-the-art results.

Contribution

It presents a novel recurrent network architecture that models relational and contextual cues jointly for person recognition in images.

Findings

Achieved state-of-the-art performance on PIPA dataset.

Effectively incorporates scene context without additional computational cost.

Demonstrates the benefit of sequence modeling for person recognition.

Abstract

Recognizing the identities of people in everyday photos is still a very challenging problem for machine vision, due to non-frontal faces, changes in clothing, location, lighting and similar. Recent studies have shown that rich relational information between people in the same photo can help in recognizing their identities. In this work, we propose to model the relational information between people as a sequence prediction task. At the core of our work is a novel recurrent network architecture, in which relational information between instances' labels and appearance are modeled jointly. In addition to relational cues, scene context is incorporated in our sequence prediction model with no additional cost. In this sense, our approach is a unified framework for modeling both contextual cues and visual appearance of person instances. Our model is trained end-to-end with a sequence of annotated instances in a photo as inputs, and a sequence of corresponding labels as targets. We demonstrate that this simple but elegant formulation achieves state-of-the-art performance on the newly released People In Photo Albums (PIPA) dataset.