This is the first part of what I call "DjFaceChop" which will crop faces out of given images. I looked at example code of face tracking all over the net and came up with this. Uses the OpenCV Haar training data to detect faces in the image. The rest is

facechop.py

import cv2

def facechop(image):
    facedata = "haarcascade_frontalface_default.xml"
    DOWNSCALE = 1
    cascade = cv2.CascadeClassifier(facedata)
    
    img = cv2.imread(image)
    
    minisize = (img.shape[1]/DOWNSCALE,img.shape[0]/DOWNSCALE)
    miniframe = cv2.resize(img, minisize)
    
    faces = cascade.detectMultiScale(miniframe)
    
    for f in faces:
        x, y, w, h = [ v*DOWNSCALE for v in f ]
        cv2.rectangle(img, (x,y), (x+w,y+h), (255,255,255))
    cv2.imshow(image, img)
    
    return
    
if __name__ == '__main__':
    facechop("beatles.jpg")
    
    while(True):
        key = cv2.waitKey(20)
        if key in [27, ord('Q'), ord('q')]: # exit on ESC
            break