Face recognition by elastic bunch graph matching
نویسندگان
چکیده
منابع مشابه
Face Recognition by Elastic Bunch Graph Matching
We present a system for recognizing human faces from single images out of a large database containing one image per person. The task is difficult because of image variation in terms of position, size, expression, and pose. The system collapses most of this variance by extracting concise face descriptions in the form of image graphs. In these, fiducial points on the face (eyes, mouth, etc.) are ...
متن کاملFace Recognition by Elastic Bunch Graph
We present a system for recognizing human faces from single images out of a large database containing one image per person. Faces are represented by labeled graphs, based on a Gabor wavelet transform. Image graphs of new faces are extracted by an elastic graph matching process and can be compared by a simple similarity function. The system diiers from the preceding one 1] in three respects. Pha...
متن کاملAverage Half Face Recognition by Elastic Bunch Graph Matching Based on Distance Measurement
Average-half-face experiments the overall accuracy of the system is better than using the original full face image. Clearly experiment shows that half face data produces higher recognition accuracy [5]. The average-half-face contain the data exactly half of the full face and thus results in storage and computational time saving. The information stored in average-half-face may be more discrimina...
متن کاملFace Recognition by Face Bunch Graph Method
Face Bunch Graph method method uses a simple comparison function both for the localization and the recognition of faces. The input data for the two processes are so-called Jets, which represent image properties in the neighbourhood of a face bunch graph (FBG) node. The algorithm described below was chosen for implementation because of its very good results and because of the application of the ...
متن کاملFace Recognition by Extending Elastic Bunch Graph Matching with Particle Swarm Optimization
Elastic Bunch Graph Matching is one of the well known methods proposed for face recognition. In this work, we propose several extensions to Elastic Bunch Graph Matching and its recent variant Landmark Model Matching. We used data from the FERET database for experimentations and to compare the proposed methods. We apply Particle Swarm Optimization to improve the face graph matching procedure in ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 1997
ISSN: 0162-8828,2160-9292
DOI: 10.1109/34.598235