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Time:
$13:20 -$ - Attendees: Bob Zhang (Supervisor), Huang Yanzhen, Mai Jiajun
Deal to the design of our MLP model, we need to specify the backside of an input. The MLP model is not trained to classify people facing back to camera.
- We defined a possible factor that can help us to know if a person is facing back to camera:
$$\mathbf{backside_ratio} = \frac{\text{shoulder_x_diff}}{\text{width_diff}}$$ - when
$\mathbf{backside_ratio} > 0$ , the person is facing to camera, and$<0$ means he/she is back to camera. - A threshold is set to be
$-0.2$ , a person will be classified as "Backside" if and only if$\mathbf{backside_ratio} < -0.2$ .
- Do we need to improve the current models?
- We are supposed to figure out the solution of wrong classification on a person wearing thick. (More data and analysis)