The effects of driving under the influence of alcohol can be severe. In an effort to reduce the number of drunk drivers, alcolocks have been deployed. However, if a driver tries to cheat the system and succeeds, the effects may cause severe damage to both humans and property. One way of beating the system is by fooling the breathalyzer that the breath produced doesn’t contain alcohol. This is usually done using a device which either filters a breath, or by having a device that blows clean air into the alcolock. In this paper the problem of if it’s possible to determine whether a true breath is made by using facial and feature detection, is answered. A true breath is an unobstructed, real breath from a person. True breath identification can be used in order to, for example, determine if a mouthpiece-less alcolock device is being used correctly. To solve this problem facial and feature detection was used. In order to find the best methods for facial and feature detection, research result in this area was used. The method used to solve this problem works by first tracking the face. The mouth is then searched for in the lower part of the face region. After that the mouth is found, the lips are to be extracted from the mouth area using various methods. The lip tracking is then used to determine the mouth state, it does this by calculating the distance between the different points produced by the lip tracking. The mouth state, mouth and face tracking are then used during the identification process to determine if a real person was present and if he/she made a true breath. The test fails if the mouth is closed during the time of breath or if there is no mouth or face present. Attempts to manipulate, result in a failed alcohol test. In the majority of the test cases the tracking and mouth state scored acceptable or better results. The performance of the system concluded that it’s possible to complete the analysis in real time. The result of this thesis is a true breath identification system. It still has some issues in cases where the illumination and lighting is uneven or bad. Facial hair may also interfere with the tracking in some cases. Although there are still some areas that needs improving, this work was more of a proof of concept. This thesis shows that it is possible, under certain circumstances, to determine a true breath using facial and feature detection.