Reading 3 : iCanDraw?

Citation:
iCanDraw: using sketch recognition and corrective feedback to assist a user in drawing human faces
D Dixon, M Prasad, T Hammond
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Publication Link

Summary:
This paper describes the second version of iCanDraw, which is essentially a software to help users learn to draw faces with step-wise guidance and live feedback. It discusses the the amends made to the previous version, which failed to yield a good experience or proper learning. At a top level, the main aim of this research is to help Left-Brained people (poor artists who draw based on a symbolic representation) adopt a gradual transition to the R-Mode (i.e Right-brained people who can perceive facial features and represent the drawing more realistically). The paper first discusses the user interface and options available to the user, and then proceeds to discuss the kind of corrective feedback mechanisms, evaluation metrics as well as the techniques which actually perform the comparison of a drawing to the actual image, by extracting a face template from both.


Discussion:
The first iteration of iCanDraw highlighted the conceptual mistakes in the program design, such as over-reliance on 'prep-sketching' and 'overtracing' to detect the user's intentions. This did not apply well for beginner drawers. The visual feedback was also not intuitive enough.

The following are some of the aspects of the user interface :

• Drawing area
• Drawing instructions
• Reference Image - It is manipulated at each step to assist in the R-Mode shift
• Corrective Feedback

Next, we discuss the implementation details outlined in the paper.

• Pre-processing reference imagery: Face recognition library is used to extract 40 points, and few are manually added to obtain 53 points.
• Setting the example template : Example template is centered and rescaled until head is drawn
• Processing of a stroke: Done using PaleoSketch
• New strokes are analyzed according to their positions as per the facial features of the features in that step using K-nearest neighbours algorithms (where k = 3) and classified accordingly. All strokes drawn outside an example template are treated as ignore spaces.

• Determining Correctness of Image: This is done using a sliding window that compares dissimilarity with the template. The average of all these distances is used to isolate windows that have a larger than acceptable standard deviation.

The paper also discusses a few principles that drawing through sketch recognition needs to follow:

1. Master template must be accurate
2. Application should complement the R-mode shift
3. Feedback should not be intrusive towards the creative process
4. Feedback should direct user towards final outcome
5. Erased strokes should be temporarily be visible
6. User should be able to override the applications suggestion, 
7. Sketch reco algo should be adaptive
8. Support with the drawing area
9. Support artistic techniques such as shading