Sketch Less Face Image Retrieval: A New Challenge

TL;DR

This paper introduces a new face image retrieval task using partial sketches, proposing a dataset and a two-stage method with a triplet network and LSTM to improve retrieval with minimal sketches.

Contribution

It presents the first framework for sketch less face image retrieval, including a dataset and a novel two-stage method combining joint embedding and sequence modeling.

Findings

Effective retrieval with partial sketches demonstrated

The dataset enables further research in sketch less face retrieval

The method outperforms baseline approaches

Abstract

In some specific scenarios, face sketch was used to identify a person. However, drawing a complete face sketch often needs skills and takes time, which hinder its widespread applicability in the practice. In this study, we proposed a new task named sketch less face image retrieval (SLFIR), in which the retrieval was carried out at each stroke and aim to retrieve the target face photo using a partial sketch with as few strokes as possible (see Fig.1). Firstly, we developed a method to generate the data of sketch with drawing process, and opened such dataset; Secondly, we proposed a two-stage method as the baseline for SLFIR that (1) A triplet network, was first adopt to learn the joint embedding space shared between the complete sketch and its target face photo; (2) Regarding the sketch drawing episode as a sequence, we designed a LSTM module to optimize the representation of the incomplete face sketch. Experiments indicate that the new framework can finish the retrieval using a partial or pool drawing sketch.