Sketch Less Face Image Retrieval: A New Challenge
Dawei Dai, Yutang Li, Liang Wang, Shiyu Fu, Shuyin Xia, Guoyin Wang

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…
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Taxonomy
TopicsFace recognition and analysis · Face and Expression Recognition · Advanced Image and Video Retrieval Techniques
MethodsSigmoid Activation · Tanh Activation · Long Short-Term Memory
