Face Detection and Face Recognition with Real time Training from a Camera. To improve the recognition performance, there are MANY things that can be improved here look at commercial Face Recognition systems such as SPOTR for examples, and some improvements can be fairly easy to implement. For example, you could add color processing, edge detection, etc. You can usually improve the face recognition accuracy by using more input images, atleast 5. If you cant take more photos, there are several simple techniques you could use to obtain more training images, by generating new images from your existing ones. You could create mirror copies of your facial images, so that you will have twice as many training images and it wont have a bias towards left or right. You could translate or resize or rotate your facial images slightly to produce many alternative images for training, so that it will be less sensitive to exact conditions. You could add image noise to have more training images that improve the tolerance to noise. Remember that it is important to have a lot of variation of conditions for each person, so that the classifier will be able to recognize the person in different lighting conditions and positions, instead of looking for specific conditions. But its also important to make sure that a set of images for a person is not too varied, such as if you rotated some images by 9. This would make the classifier to be too generic and also give very bad results, so if you think you will have a set of images with too much variance such as rotation more than 2. Dlib contains a wide range of machine learning algorithms. All designed to be highly modular, quick to execute, and simple to use via a clean and. Face-Recognition-Women.jpg' alt='Back Face Detection C Program' title='Back Face Detection C Program' />Learn how National Breast Cancer Foundation helps women now by funding targeted research projects. Radar is an objectdetection system that uses radio waves to determine the range, angle, or velocity of objects. It can be used to detect aircraft, ships, spacecraft. KHDCz.png' alt='Back Face Detection C Program' title='Back Face Detection C Program' />For example, you could train a classifier to recognize JohnFacingForward and another one for JohnFacingLeft and other ones MaryFacingForward, MaryFacingLeft, etc. Frontpage 2003 Arabic. Then each classifier can have a lot of variance but not too much, and you simply need to associate the different classifiers for each person with that one person ie John or Mary. Thats why you can often get very bad results if you dont use good preprocessing on your images. As I mentioned earlier, Histogram Equalization is a very basic image preprocessing method that can make things worse in some situations, so you will probably have to combine several different methods until you get decent results. And something important to understand is that at the heart of the algorithm, it is matching images by basically doing the equivalent of subtracting the testing image with a training image to see how similar they are. This would work fairly well if a human performed it, but the computer just thinks in terms of pixels and numbers. So if you imagine that it is looking at one pixel in the test image, and subtracting the greyscale value of that pixel with the value of the pixel in the EXACT same location of each training image, and the lower the difference then the better the match. Back Face Detection C Program' title='Back Face Detection C Program' />So if you just move an image by a few pixels across, or use an image that is just a few pixels bigger or has a few more pixels of the forehead showing than the other image, etc, then it will think they are completely different images This is also true if the background is different, because the code doesnt know the difference between background and foreground face, which is why its important to crop away as much of the. Since the images should be almost perfectly aligned, it actually means that in many cases, using small low res images such as by shrinking the images to thumbnail size can give better recognition results than large hi res images Because they will be better aligned if they are shrunk. Also, even if the images are perfectly aligned, if the testing image is a bit brighter than the training image then it will still think there is not much of a match. YsEXPYCsTg/0.jpg' alt='Back Face Detection C Program' title='Back Face Detection C Program' />Histogram Equalization can help in many cases but it can also make things worse in other cases, so differences in lighting is a difficult common problem. There are also issues such as if there was a shadow on the left of the nose in the training image and on the right in the test image then it will often cause a bad match, etc. Thats why face recognition is relatively easy to do in realtime if you are training on someone and then instantly trying to recognize them after, since it will be the same camera, and background will be the same, their expressions will be almost the same, the lighting will be the same, and the direction you are viewing them from will be the same. So you will often get good recognition results at that moment. But once you try to recognize them from a different direction or from a different room or outside or on a different time of the day, it will often give bad results So its important to do a lot of experimentation if you want better results, and if you still cant get good results after trying many things, perhaps you will need a more complicated face recognition algorithm than PCA Eigenfaces, such as 3. D Face Recognition or Active Appearance Models, mentioned below. Something you should know is that Eigenfaces is considered the simplest method of accurate face recognition, but many other much more complicated methods or combinations of multiple methods are slightly more accurate. So if you have tried the hints above for improving your training database and preprocessing but you still need more accuracy, you will probably need to learn some more complicated methods, or for example you could figure out how to combine separate Eigenface models for the eyes, nose mouth. Most tutorials on face recognition are for basic Neural Networks, which usually dont work as well as Eigenfaces does. And unfortunately there are only some basic explanations for better type of face recognition than Eigenfaces, such as recognition from video and other techniques at the Face Recognition Homepage, or 3. D Face Recognition and Active Appearance Models. But for other techniques, you should read some recent computer vision research papers from CVPR and other computer vision conferences. Most computer vision or machine vision conferences include new advances in face detection and face recognition that give slightly better accuracy. So for example you can look for the CVPR1. CVPR0. 9 conferences at http www. Marguerite Duras The War Pdf'>Marguerite Duras The War Pdf. There are also several better Face. Recognition implementations inside the CSU Face Identification Evaluation System. But remember that when you are implementing state of art techniques such as this, you usually wont be able to find any information or help about it except for what is in the 1 or 2 research papers about the technique. So you should only do it if you are confident at getting it to work by yourself If you had trouble understanding this tutorial then you will probably have much more trouble understanding a conference paper about a more complicated technique.