faces = faceCascade.detectMultiScale(
    gray,
    scaleFactor=1.1,
    minNeighbors=5,
    minSize=(30, 30),
    flags = cv2.cv.CV_HAAR_SCALE_IMAGE
)

for (x, y, w, h) in faces:
    mid_x = x + int(w/2)
    mid_y = y + int(h/2)

height, width = img.shape
x_dot = ...
y_dot = ...

dx_dot = int(width/2) - x_dot
dy_dot = int(height/2) - y_dot

M = np.float32([[1,0,dx_dot],[0,1,dy_dot]])
dst = cv2.warpAffine(img,M,(cols,rows))

p_1 = [eyes_x, eye_y]
    p_2 = [int(width/2),int(height/2)] # default: center of the image
    p_3 = [mouth_x, mouth_y]

    target_p_1 = [eyes_x, int(eye_y * 0.45)]
    target_p_2 = [int(width/2),int(height/2)] # don't want to change
    target_p_3 = [mouth_x, int(mouth_y * 0.75)]

    pts1 = np.float32([p_1,p_2,p_3])
    pts2 = np.float32([target_p_1,target_p_2,target_p_3])

    M = cv2.getAffineTransform(pts1,pts2)

    output = cv2.warpAffine(image,M,(height,width))