There is one example python program in dlib to detect the face landmark position. face_landmark_detection.py
This program detect the face feature and denote the landmark
I slightly modified the code.
import dlib
import numpy as np
from skimage import io
predictor_path = "shape_predictor_68_face_landmarks.dat"
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)
img = io.imread("FDT.jpg")
dets = detector(img)
#output face landmark points inside retangle
#shape is points datatype
#http://dlib.net/python/#dlib.point
for k, d in enumerate(dets):
shape = predictor(img, d)
vec = np.empty([68, 2], dtype = int)
for b in range(68):
vec[b][0] = shape.part(b).x
vec[b][1] = shape.part(b).y
print(vec)
Here is the output
[[1003 575]
[1005 593]
[1009 611]
[1014 627]
[1021 642]
[1030 655]
[1041 667]
[1054 675]
[1069 677]
[1083 673]
[1095 664]
[1105 651]
[1113 636]
[1120 621]
[1123 604]
[1124 585]
[1124 567]
[1010 574]
[1020 570]
[1031 571]
[1042 574]
[1053 578]
[1070 577]
[1081 572]
[1092 568]
[1104 566]
[1114 569]
[1063 589]
[1063 601]
[1063 613]
[1063 624]
[1050 628]
[1056 630]
[1064 632]
[1071 630]
[1077 627]
[1024 587]
[1032 587]
[1040 586]
[1048 588]
[1040 590]
[1031 590]
[1078 587]
[1085 585]
[1093 584]
[1101 584]
[1094 588]
[1086 588]
[1045 644]
[1052 641]
[1058 640]
[1064 641]
[1070 639]
[1078 640]
[1086 641]
[1080 651]
[1073 655]
[1066 656]
[1059 656]
[1052 652]
[1048 645]
[1059 645]
[1065 646]
[1071 644]
[1083 642]
[1072 645]
[1065 647]
[1059 646]]
And there is another open source project OpenFace, which is based on dlib and describes each point's correlating part in face.
The url of describing image