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Difference between revisions of "IS480 Team wiki: 2012T1 M.O.O.T/Project Management/GR Metrics"

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| style="background: #808080; color: #FFFFFF; width: 30%; text-align:center;" | <b>Action plan</b>
 
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| style="text-align:center;"|Accuracy < 40%
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| style="text-align:center;"|Accuracy ≤ 50%
 
| Hold team meeting to consider dropping gender recognition if accuracy still does not improve after several iterations
 
| Hold team meeting to consider dropping gender recognition if accuracy still does not improve after several iterations
 
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| style="text-align:center;"|40% Accuracy ≤ 70%
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| style="text-align:center;"|50% < Accuracy ≤ 70%
 
| Search for more ways to improve the accuracy either by improving the current methods used to detect gender or look for new parameters that prove useful for gender recognition
 
| Search for more ways to improve the accuracy either by improving the current methods used to detect gender or look for new parameters that prove useful for gender recognition
 
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Revision as of 18:18, 5 October 2012

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Project Overview

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Technical Applications


Project Schedule Schedule & Bug Metrics Gender Recognition Metrics Risk Management Minutes Repository

Gender Recognition Metrics

As gender recognition using Kinect is a newly-ventured area and there is no API available in the Kinect SDK that is able to detect gender, it is essential for us to use some form of metrics to gauge the progress of our gender recognition algorithm. To ensure a fair test, we made sure our testers included a good mix of males and females of different body proportions and wearing different kinds of clothes.

We plan to have 30 testers – 15 males and 15 females and will record a short video of all 30 individuals standing in front of AlterSense using Kinect Studio. As Kinect Studio is a tool that allows the recording of a session with the kinect and playing back of the recorded session, we could replay the videos on Kinect Studio each time we have made progress on our gender recognition algorithm. The usage of the same testers for each testing ensures that we can have a good comparison on the before and after of our algorithm.

How We Detect Gender

Initially, we had 5 parameters to determine gender:

  • Height

It is widely known that males are generally taller than females. Also, as East Asians tend to be of a smaller build than Caucasians, we took 1.7m to differentiate between males and females i.e. majority of females tend to be shorter than 1.7m while many males tend to be taller than 1.7m. There are exceptions to this as there exists several females who are taller than 1.7m and males shorter than 1.7m, hence we have taken other factors into consideration.

  • Presence of bag

It is observed that only females would carry their bags on their elbow. Thus when it is detected that a person is carrying a bag on the elbow, it is highly possible that the person is a female.

  • Presence of long hair

The majority of people who have long hair in Singapore are normally female. There may be a few exceptions but it is very very rare. By calculating the width of a person’s neck, we can be quite sure that the outline of a person with wider neck than usual is a female as the long hair is the one contributing to the width of the neck.

  • Presence of long skirt

Currently, females are the only ones who would wear a skirt in Singapore. By calculating the width of the hem of the skirt and the slope of the skirt, we can distinguish skirts from shorts and pants as shorts and pants do not have a significant slope compared to skirts. Unfortunately, it is not possible to differentiate shorts and pants from a tight-fitting skirt.

  • Shoulder width & Center of moment

Based on our research, males are proven to have a larger center of moment value than females given similar height and weight. This is because males tend to have broader shoulder than females while females generally have wider hips than males. Based on this, we attempt to differentiate a male from a female of similar build, has short hair and does not wear a skirt nor carry a bag on her elbow by their center of moment value.

After a round of testing during our User Testing 1, we have decided to drop the detection of bag parameter because Kinect is unable to track the arm coordinates of a tester accurately when the tester is carrying a bag on his/her shoulders as the Kinect would detect the bag as part of the arm. Hence we would have difficulties determining whether a person is carrying a bag on the elbow.

Therefore we are left with 4 parameters to determine gender currently:

  • Height
  • Presence of long hair
  • Presence of long skirt
  • Shoulder width & Center of moment

Testers' profile

Based on the 5 (now 4) parameters listed above, we pick our testers in the following way:

Parameter Female Male
Height 5 with height ≤ 1.6m
5 with height > 1.6m but < 1.7m
5 with height ≥ 1.7m
5 with height ≤ 1.7m
5 with height > 1.7m, but < 1.8m
5 with height ≥ 1.8m
Bag [dropped] 8 carrying bag on their elbow
7 not carrying bag on their elbow
N.A
Hair 7 with short hair (does not touch the shoulders)
8 with shoulder-length or longer hair
N.A
Skirt 7 not wearing skirt
8 wearing skirt
N.A
Center of moment (COM) We will attempt to find ≥5 pairs of females and males that have similar height and similar body proportions

Metrics

Click to see the details of Gender Recognition Metrics: Gender Recognition Metrics

Version Changes made to algorithm Accuracy of presence of skirt Accuracy of presence of hair Accuracy of presence of bag Overall accuracy of gender detected Comment
1 5 parameters are done but only 2 parameters - height and shoulder & hip width are integrated in the Neural Network. (meaning gender is determined by height and shoulder & hip width for this version) 62.86% 38.57% 22.86% 87.14% Currently the gender recognition method is accurate only when the tester is standing at a specific distance from the Kinect.

We have decided to drop the detection of bag parameter due to 2 reasons stated here.
2 The other 2 parameters - hair and skirt are integrated in the Neural Network
3 Included slope of skirt
Overall accuracy Action plan
Accuracy ≤ 50% Hold team meeting to consider dropping gender recognition if accuracy still does not improve after several iterations
50% < Accuracy ≤ 70% Search for more ways to improve the accuracy either by improving the current methods used to detect gender or look for new parameters that prove useful for gender recognition
Accuracy > 70% May consider improvising gender recognition algorithm but not deemed necessary