27-02-2012, 10:50 PM
i want more details of atleast 25 pages of documentation for Face Recognition technology.
27-02-2012, 10:50 PM
i want more details of atleast 25 pages of documentation for Face Recognition technology.
20-03-2012, 10:07 AM
i am B.E final year student.i want seminar report for face recognition.
01-12-2012, 06:17 PM
Face Recognition
Face Recognition.pptx (Size: 1.66 MB / Downloads: 69) Abstract Images play an important role in todays information because A single image represents a thousand words. Google's image search, where we can easily search for images using keywords. Getting the computer to understand the semantics inside of images isn't easy. The reason for this is simply because the computer isn't able to understand the context. Introduction Face recognition has become a popular area of research in computer vision and one of the most successful applications of image analysis and understanding. A set of two task: Face Identification: Given a face image that belongs to a person in a database, tell whose image it is. Face Verification: Given a face image that might not belong to the database, verify whether it is from the person it is claimed to be in the database. Feature extraction methods Feature extraction is the task where we locate facial features, Eg: the eyes, the nose, and the chins etc. This task may be performed after the face detection task Or recognition time. big challenge for feature extraction methods is feature “restoration“. Facial features are invisible according to the large variation. Holistic methods Holistic methods uses the whole face region as the input to a recognition system. focuses a holistic method using eigenfaces to recognize still faces. Face Recognition Using Eigenfaces The weight of the incoming unknown image is found and then compared to the weights of those already in the system. If the input image's weight is over a given threshold it is considered to be unknown. The identification of the input image is done by finding the image in the database whose weights are the closest to the weights of the input image. The image in the database with the closest weight will be returned as a hit to the user of the system. 3D morphable model Took face recognition to a new level. By being able to use a morphable 3D model to create synthetic images has proven to give good results. It is a very applicable approach that solves many of the problems. Problems of Face Recognition when comparing a database image with an input image. The main concern is of course that all images of the same face are heterogeneous. When image databases are created they contain good scenario images. concerning deferent facial expressions as well. The system must be able to know that two images of the same person with deferent facial expressions actually is the same person. makeup, posing positions, illumination conditions, and comparing images of the same person with and without glasses. CONCLUSION Introduction of face Recognition How facial recognition works. Face detection and recognition. Different approaches of face Recognition. Feature extraction methods Holistic methods Hybrid methods Problems
22-12-2012, 11:40 AM
to get information about the topic "face recognition" full report ppt and related topic refer the link bellow
https://seminarproject.net/Thread-face-r...nar-report https://seminarproject.net/Thread-face-r...rt?page=10 https://seminarproject.net/Thread-face-r...ort?page=4 https://seminarproject.net/Thread-face-r...ort?page=9 http://project-seminars.com/attachment.php?aid=43672 https://seminarproject.net/Thread-high-s...nition-ppt https://seminarproject.net/Thread-semina...umentation |
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