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Using the Matlab command ginput to isolate the mouth from an image and then performing tests to detect mood we had the following results.
| Subject # | Input Sequence | Output | Accuracy |
| Subject 1 | Angry, Happy, Sad, Surprised | Angry, Happy, Sad, Surprised | 100% |
| Subject 2 | Happy, Surprised, Sad, Angry | Happy, Surprised, Sad, Angry | 100% |
| Subject 3 | Surprised, Sad, Angry, Happy | Surprised, Sad, Angry, Happy | 100% |
| Subject 4 | Sad, Happy, Surprised, Angry | Angry, Happy, Surprised, Sad | 50% |
| Subject 5 | Angry, Happy, Sad, Surprised | Angry, Happy, Sad, Surprised | 100% |
| Subject 6 | Happy, Surprised, Sad, Angry | Happy, Surprised, Sad, Angry | 100% |
| Subject 7 | Surprised, Sad, Angry, Happy | Surprised, Sad, Angry, Happy | 100% |
| Subject 8 | Sad, Happy, Surprised, Angry | Sad, Surprised, Happy, Angry | 50% |
| Subject 9 | Angry, Happy, Sad,Surprised | Angry, Happy, Sad,Surprised | 100% |
| Subject 10 | Happy, Surprised,Sad, Angry | Happy, Surprised,Sad, Angry | 100% |
| Subject 11 | Surprised, Sad, Happy, Angry | Surprised, Angry, Happy, Sad | 50% |
| Subject 12 | Sad, Happy, Surprised, Angry | Angry, Happy, Surprised, Sad | 50% |
We then ran the test using the function goodcrop instead of doing the cropping manually using ginput. We obtained the following results
| Subject # | Input Sequence | Output | Accuracy |
| Subject 1 | Angry, Happy, Sad, Surprised | Angry, Happy, Sad, Surprised | 100% |
| Subject 2 | Angry, Happy, Sad, Surprised | Angry, Happy, Sad, Surprised | 100% |
| Subject 3 | Angry, Happy, Sad, Surprised | Sad, Happy, Angry, Surprised | 50% |
| Subject 4 | Angry, Happy, Sad, Surprised | Surprised, Happy, Angry, Sad | 25% |
| Subject 5 | Angry, Happy, Sad, Surprised | Angry, Happy, Sad, Surprised | 100% |
| Subject 6 | Angry, Happy, Sad, Surprised | Sad, Surprised, Happy, Angry | 25% |
| Subject 7 | Angry, Happy, Sad, Surprised | Angry, Happy, Sad, Surprised | 100% |
| Subject 8 | Angry, Happy, Sad, Surprised | Angry, Surprised, Happy, Sad | 25% |
| Subject 9 | Angry, Happy, Sad, Surprised | Angry, Happy, Sad, Surprised | 100% |
| Subject 10 | Angry, Happy, Sad, Surprised | Angry, Happy, Sad, Surprised | 100% |
| Subject 11 | Angry, Happy, Sad, Surprised | Angry, Happy, Sad, Surprised | 100% |
| Subject 12 | Angry, Happy, Sad, Surprised | Sad, Happy, Surprised, Angry | 25% |
The overall accuracy of the mood detection algorithm , when using the matlab function ginput, was 83%. The overall accuracy when using the goodcrop routine was 71%.
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