Sketch Less Face Image Retrieval: A New Challenge
Dawei Dai, Yutang Li, Liang Wang, Shiyu Fu, Shuyin Xia, Guoyin Wang
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 poor drawing sketch. (https://github.com/ddw2AIGROUP2CQUPT/SLFIR)
BibTeX
@inproceedings{icassp2023_sketchlessfaceim,
title = {Sketch Less Face Image Retrieval: A New Challenge},
author = {Dawei Dai and Yutang Li and Liang Wang and Shiyu Fu and Shuyin Xia and Guoyin Wang},
booktitle = {ICASSP 2023},
year = {2023}
}