gure 2: Eigenfaces of the training set [26] 2) Calculate the eigenfaces from the training set, keeping only the M images that correspond to the highest eigenvalues. These M images define the face space. As new faces are experienced, the eigenfaces can be updated or recalculated. 3) Calculate the corresponding distribution in M-dimensional weight space for each known individual, by projecting their face images onto the ‘face space’. After the initialization operations, there are carried out more operations in order to recognize new face images. 4) Calculate a set of weights based on the input image and the M eigenfaces by projecting the input image onto each of the eigenfaces. 5) Determine if the image is a face at all by checking to see if the image is sufficiently close to ‘face space’. 6) If it is a face, classify the weight pattern as either a known person or as unknown. 7) (Optional) Update the eigenfaces and/or weight patterns. 8) (Optional) If the same unknown face is seen several times, calculate its characteristic weight pattern and incorporate into the known faces[24]. As mentioned earlier, there is a long list of methods that can be used for facial recognition. Four of them, i.e Eigenface Method, Correlation Method, Fisherface Method and the Linear Subspaces Method, are the most favorite. Below here, you can find the error rates of those four methods, considered pictures with close crop or the whole face. Figure 3: Graph and table of the result of an experiment with the four most used facial recognition techniques [1] As you can see, the Eigenface Method has the most errors, and the Fisherface Method the least. You can also see that the error rate is higher with images of close crops faces, compared to a full face image. This shows that it is harder for a facial recognition algorithm to recognize someone if their face is not fully shown in the picture and the features are thus not recognized. It also reminds us of the fact that facial recognition techniques are not completely accurate. Hopefully they will become more accurate in the future, so e.g crime can be prevented faster and better. 3. Results of face recognition technologies in crime prevention There are many ways for law enforcement to help them with decreasing the amount of crime. Face recognition has a big role in human life. Witnesses can describe a person’s face. Also, the citizenry can help by sending in photos. But an Artificial Intelligence can also do face recognition. It can search through a database full of mug shots to find a match with the face from an image or sketch. The Artificial Intelligence will take much shorter time to find a match than a group of officers or even specialists. In the following part, there will be discussed how four different approaches are all playing a role in crime prevention.>