I am using shape_predictor_81_face_landmarks.dat in order to obtain facial landmarks. Here is the result:
As you you can see this is the points are actually 2D numpy arrays:
[[201 122]
[201 140]
[202 157]
[204 174]
[209 190]
[219 205]
[230 219]
[244 231]
[261 236]
[278 233]
[292 222]
[304 209]
[314 195]
[320 179]
[324 163]
[325 147]
[327 130]
[214 119]
[223 112]
[236 111]
[248 113]
[260 118]
[277 119]
[289 116]
[301 116]
[312 119]
[319 127]
[268 127]
[267 138]
[267 148]
[267 160]
[250 164]
[258 167]
[266 170]
[274 168]
[281 165]
[228 125]
[235 122]
[243 123]
[251 128]
[242 127]
[235 127]
[283 131]
[291 127]
[298 127]
[305 131]
[298 132]
[291 131]
[231 178]
[243 177]
[256 178]
[265 180]
[274 179]
[286 180]
[296 182]
[285 196]
[273 202]
[263 203]
[254 201]
[241 193]
[235 179]
[255 181]
[264 183]
[274 182]
[293 183]
[273 195]
[264 196]
[255 194]
[219 73]
[232 73]
[252 78]
[274 80]
[305 78]
[318 82]
[328 108]
[209 91]
[216 79]
[201 114]
[328 124]
[321 92]
[297 81]]
From such data, I want to be able predict people personnality according to some pseudo-science labeling. How should I approach this problem? Should I train some model? Or just go with some raw algorithmes.
>Solution :
As far a s I understand from the question you want to predict the personality of the people in the image. I don’t know the exact application you want but you can use the big five personality dimensions as personality classes. Afterwards, you can use a Face – Personality dataset such as The Basel Face Database (BFD) to train a NN in order to make personality predictions from face images.