Face Photo-Sketch Recognition Using Bidirectional Collaborative Synthesis Network

TL;DR

This paper proposes a deep learning framework using a bidirectional collaborative synthesis network with an intermediate latent space to improve face photo-sketch recognition, effectively addressing modality gap and limited training data.

Contribution

It introduces a novel bidirectional synthesis network with a rich latent space and a three-step training scheme for face photo-sketch matching.

Findings

Outperforms existing state-of-the-art methods on public datasets.

Effectively reduces modality gap in face sketch recognition.

Demonstrates versatility for other modality matching tasks.

Abstract

This research features a deep-learning based framework to address the problem of matching a given face sketch image against a face photo database. The problem of photo-sketch matching is challenging because 1) there is large modality gap between photo and sketch, and 2) the number of paired training samples is insufficient to train deep learning based networks. To circumvent the problem of large modality gap, our approach is to use an intermediate latent space between the two modalities. We effectively align the distributions of the two modalities in this latent space by employing a bidirectional (photo -> sketch and sketch -> photo) collaborative synthesis network. A StyleGAN-like architecture is utilized to make the intermediate latent space be equipped with rich representation power. To resolve the problem of insufficient training samples, we introduce a three-step training scheme. Extensive evaluation on public composite face sketch database confirms superior performance of our method compared to existing state-of-the-art methods. The proposed methodology can be employed in matching other modality pairs.