Object Tracking and Face Recognition in Video Streams

28 Chapter 6. Conclusionsfulfilled by having integrated Faceclip, and having compared it to the original code. Becauseof negative results, Faceclip is not used in the current implementation. While it would havebeen nice to have goals G2 and G4 implemented too, the results of the current systemare enough to give an indication of the effect of using object tracking in a face recognitionsystem.The conclusion drawn from this project is that object tracking is a good tool for improvingthe accuracy of face recognition in video streams. Anyone implementing face recognitionfor video streams should consider using object tracking as a central component.6.1 Limitations and future workTesting was done by comparing whether a reported face position overlaps with a knownface position. It would, for example, mark a position defining a person’s nose as correct,since it overlaps with the whole face. Inspecting the results shows that there are few suchoccasions, but it still means that the result numbers are not fully accurate.The current object tracking implementation cannot handle a large number of false detectionswith the unknown PID, as it uses up too much memory.The current way for filtering false detections easily fails; a trajectory will be acceptedas long as the face detector reports a particular image patch twice within a short timespan.This is particularly apparent when combining object tracking with Faceclip, as Faceclipoften reports the same invalid image patch twice or more.One type of false detection that occurs is when the system tracks a face properly for awhile, and then finds the face at the wrong position for a few frames, and then goes back totracking it at the correct position. A solution based on continuity filtering (Nielsen, 2010)may be a way to get rid of the false intermediate positions.It may also be useful to try identifying faces reported by the object tracker, instead ofthe current way of only identifying faces reported by the face detector. Doing this wouldincrease the number of faces for potential identification.It would be useful to test different settings, for example other values of pid min and det min .That may be enough to get rid of some of the false detections and identifications.The system currently only does forward tracking. As mentioned in Section ??, onepossible improvement would be to also implement backward tracking.The optional goal of detecting the direction of each face has not been implemented. Afuture research idea is to look into techniques for doing that.In the Complex experiment, the object tracker failed to track some faces. As mentionedin Section 5.2, it may be due to the low frame rate. One way to test whether it is causedby the low frame rate may be to down-sample the frame rate of a video clip with a higherframe rate, and see how the object tracking is affected. If the low frame rate is the cause ofthe failure, getting good results when using the implementation may require a video sourcewith a high enough frame rate.In general, Wawo seems to be quite sensitive to what training pictures are included;they all have to be of similar size and quality. Because of the high sensitivity, using theimplementation in practice may require the user to put some effort into producing trainingpictures of high enough, and consistent, quality.