Comparing faces"

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(TriDimRegression)
library(dplyr)
library(ggplot2)
library(tidyr)

Tridimensional regression can be used to compare face similarity via R2 based on 3D landmarks.

faces <- 
  list("M010"=Face3D_M010,
       "M101"=Face3D_M101,
       "M244"=Face3D_M244,
       "M92"=Face3D_M92,
       "W070"=Face3D_W070,
       "W097"=Face3D_W097,
       "W182"=Face3D_W182,
       "W243"=Face3D_W243)


face_comparison <- 
  dplyr::as_tibble(t(combn(names(faces), 2))) %>%
  rename(Face1 = V1, Face2 = V2 ) %>%
  group_by(Face1, Face2) %>%
  nest() %>%
  mutate(Fit = purrr::map2(Face1, 
                           Face2, 
                           ~fit_transformation_df(faces[[.x]],
                                                  faces[[.y]],
                                                  transformation ='translation')))

face_R2 <-
  face_comparison %>%
  mutate(R2 = purrr::map(Fit, ~R2(.))) %>%
  select(Face1, Face2, R2) %>%
  unnest(cols=c(R2)) %>%
  arrange(desc(R2))

knitr::kable(face_R2, digits = c(0, 0, 3, 3, 3))

ggplot(data=face_R2, aes(x=Face1, y=Face2, size=R2, color=R2)) + 
  geom_point()


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TriDimRegression documentation built on Oct. 5, 2021, 9:11 a.m.