Comparing faces

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)), 
                   .name_repair = function(x){c("Face1", "Face2")}) %>%
  group_by(Face1, Face2) %>%
  nest() %>%
  mutate(Fit = purrr::map2(Face1, 
                           Face2, 
                           ~fit_transformation_df(faces[[.x]],
                                                  faces[[.y]],
                                                  transformation ='translation',
                                                  refresh=0)))

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))

Face1	Face2	R2	R2_5.5	R2_94.5
W070	W097	0.988	0.987	0.988
M010	W243	0.984	0.984	0.985
M244	W243	0.984	0.983	0.984
W182	W243	0.980	0.980	0.981
M010	M92	0.979	0.978	0.980
W097	W243	0.978	0.978	0.979
M010	M244	0.978	0.978	0.979
M92	W243	0.975	0.975	0.976
W070	W243	0.975	0.974	0.975
M010	M101	0.975	0.974	0.975
M244	W182	0.975	0.973	0.975
M92	W182	0.973	0.972	0.974
M101	W243	0.973	0.972	0.974
W070	W182	0.972	0.971	0.973
W097	W182	0.971	0.969	0.971
M244	M92	0.969	0.968	0.969
M101	M244	0.967	0.966	0.968
M010	W182	0.967	0.965	0.968
M101	W097	0.965	0.963	0.966
M101	M92	0.962	0.961	0.963
M101	W070	0.962	0.960	0.963
M010	W070	0.962	0.960	0.963
M010	W097	0.961	0.959	0.962
M244	W097	0.961	0.959	0.962
M101	W182	0.955	0.953	0.957
M244	W070	0.955	0.953	0.956
M92	W070	0.948	0.945	0.949
M92	W097	0.946	0.944	0.948

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

Alexander (Sasha) Pastukhov

2022-02-17