def KNNClassifier(training, test, k, metric='euclidean'):
''' Uses the training data to classify the observations in testing data
using the nearest neighbor approach
'''
# Remove the class column of training to use in the cdist function
noclass_training = remove_class(training)
# Set up the array of labels for test
label_array = np.empty(len(test), dtype=int)
label_array.shape = (len(test),1)
distance = d(test,noclass_training,metric)
# Each row of distances between one test observation to all other
# observations in training
for (i, di) in enumerate(distance):
# Sort the distances of this row and return their sorted indeces
idx_array = np.argsort(di)
# A list of all the classes of the kth nearest neighbors
# Take the first k sorted indeces and return their corresponding class
# from training
cl_array = np.array([x[0] for x in training[idx_array[0:k]]])
# Find the most occurring class
cl = mode(cl_array)
label_array[i] = cl
return label_array
def geometricFeatures1(self, img, landmarks=None):
"""Improved Performance in Facial Expression Recognition Using 32 Geometric Features
Linear features(15):
– 3 for left eyebrow
– 2 for left eye
– 1 for cheeks
– 1 for nose
– 8 for mouth
Polygonal features(3):
– 1 for the left eye
– 1 between corners of left eye and left corner of mouth
– 1 for mouth
Elliptical features(7):
– 1 for left eyebrow
– 3 for the left eye: eye, upper and lower eyelids
– 3 for mouth: upper and lower lips
Slope features(7):
– 1 for left eyebrow
– 6 for mouth corners
"""
pts = self.faceAlignment.get_landmarks(
img)[0] if landmarks is None else landmarks
result = []
# Linear features
#eyebrow
result.append(d(pts[21], pts[22]))
result.append(d(pts[22], pts[42]))
result.append(d(pts[26], pts[45]))
#left eye
result.append(d(pts[42], pts[45]))
#d2=43-47 || 44-46
result.append(d(pts[43], pts[47]))
#cheeks
result.append(d(pts[1], pts[15]))
#nose
result.append(d(pts[31], pts[35]))
#mouth
result.append(d(pts[48], pts[51]))
result.append(d(pts[51], pts[54]))
result.append(d(pts[62], pts[66]))
result.append(d(pts[51], pts[57]))
result.append(d(pts[50], pts[33]))
result.append(d(pts[52], pts[33]))
result.append(d(pts[48], pts[31]))
result.append(d(pts[54], pts[35]))
#Polygonal features:
result.append(FaceTools._polyArea([pts[48], pts[54], pts[57]]))
result.append(FaceTools._polyArea([pts[54], pts[42], pts[45]]))
result.append(FaceTools._polyArea([pts[42], pts[22], pts[26],
pts[45]]))
#Slope features:
result.append(FaceTools._slope(pts[22], pts[26]))
result.append(FaceTools._slope(pts[48], pts[31]))
result.append(FaceTools._slope(pts[54], pts[35]))
result.append(FaceTools._slope(pts[48], pts[51]))
result.append(FaceTools._slope(pts[51], pts[54]))
result.append(FaceTools._slope(pts[54], pts[57]))
result.append(FaceTools._slope(pts[48], pts[57]))
return result
def faceDistances(self, img, landmarks=None):
""" calculate distances as described in
'Automatic Facial Expression Recognition Using Combined Geometric Features':
D1 Left eyebrow length
D2 Right eyebrow length
D3 Distance between left and right eyebrow
D4 Left eye height
D5 Left eye width
D6 Right eye height
D7 Right eye width
D8 Distance between left eyebrow and left eye
D9 Distance between right eyebrow and right eye
D10 Distance between nose tip and upper lip
D11 Lip width
D12 Lip height
D13 Inner lip distance
D14 Distance between left eye corner and lip left corner
D15 Distance between right eye corner and lip right corner
"""
pts = self.faceAlignment.get_landmarks(
img)[0] if landmarks is None else landmarks
distances = [
d(pts[17], pts[18]) + d(pts[18], pts[19]) + d(pts[19], pts[20]) + \
d(pts[20], pts[21]),
d(pts[22], pts[23]) + d(pts[23], pts[24]) + d(pts[24], pts[25]) + \
d(pts[25], pts[26]),
d(pts[21], pts[22]),
d(pts[40], pts[38]),
d(pts[36], pts[39]),
d(pts[43], pts[47]),
d(pts[42], pts[45]),
d(pts[19], pts[37]),
d(pts[24], pts[44]),
d(pts[33], pts[51]),
d(pts[48], pts[54]),
d(pts[51], pts[57]),
d(pts[62], pts[66]),
d(pts[36], pts[48]),
d(pts[45], pts[54])
]
return distances
