HAIFAI: Human-AI Interaction for Mental Face Reconstruction

TL;DR

HAIFAI is an interactive system that combines human input and AI to reconstruct faces from mental images, outperforming previous methods in quality, usability, and speed through a two-stage process involving iterative ranking and manual refinement.

Contribution

This paper introduces HAIFAI, a novel two-stage human-AI interaction system for mental face reconstruction, including a computational user model and a new face ranking dataset.

Findings

Outperforms previous state-of-the-art in reconstruction quality.

Achieves a 60.6% face identification rate.

Demonstrates improved usability and reduced workload.

Abstract

We present HAIFAI - a novel two-stage system where humans and AI interact to tackle the challenging task of reconstructing a visual representation of a face that exists only in a person's mind. In the first stage, users iteratively rank images our reconstruction system presents based on their resemblance to a mental image. These rankings, in turn, allow the system to extract relevant image features, fuse them into a unified feature vector, and use a generative model to produce an initial reconstruction of the mental image. The second stage leverages an existing face editing method, allowing users to manually refine and further improve this reconstruction using an easy-to-use slider interface for face shape manipulation. To avoid the need for tedious human data collection for training the reconstruction system, we introduce a computational user model of human ranking behaviour. For this, we collected a small face ranking dataset through an online crowd-sourcing study containing data from 275 participants. We evaluate HAIFAI and an ablated version in a 12-participant user study and demonstrate that our approach outperforms the previous state of the art regarding reconstruction quality, usability, perceived workload, and reconstruction speed. We further validate the reconstructions in a subsequent face ranking study with 18 participants and show that HAIFAI achieves a new state-of-the-art identification rate of 60.6%. These findings represent a significant advancement towards developing new interactive intelligent systems capable of reliably and effortlessly reconstructing a user's mental image.