Demo Data from SIGIR 2026 / reviewers in the wild / expert
Daniel Dixon
dblp:42/7983
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2010
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Learning and educational technologies · 50% Interaction techniques and input · 50% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Interaction techniques and input › sketch-based interaction
sketch recognition |
0.1 | 1 | 2010 | iCanDraw: using sketch recognition and corrective feedback to assist a user in drawing human faces · CHI 2010 |
Methods — techniques the papers use, named apart from their topics
sketch recognition · 0.1face recognition · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | iCanDraw: using sketch recognition and corrective feedback to assist a user in drawing human facesabstractWhen asked to draw, many people are hesitant because they consider themselves unable to draw well. This paper describes the first system for a computer to provide direction and feedback for assisting a user to draw a human face as accurately as possible from an image. Face recognition is first used to model the features of a human face in an image, which the user wishes to replicate. Novel sketch recognition algorithms were developed to use the information provided by the face recognition to evaluate the hand-drawn face. Two design iterations and user studies led to nine design principles for providing such instruction, presenting reference media, giving corrective feedback, and receiving actions from the user. The result is a proof-of-concept application that can guide a person through step-by-step instruction and generated feedback toward producing his/her own sketch of a human face in a reference image. Daniel Dixon, Manoj Prasad, Tracy Anne Hammond |
CHI | 1 |