Unsupervised Feature Based Key-frame
Extraction
Towards Face Recognition
Jana Selvaganesan1, and Kannan Natarajan2
1Department
of ECE, Mookambigai College of Engineering, India
2Department
of CSE, Jayaram College of Engineering and Technology, India
Abstract: A
convenient and most effective method of querying a video database for robust
face recognition is by using key-frames extracted from the image sequence. In
this paper we present a clustering based approach that bypasses the need for
shot detection or segmentation, to extract the key-frames from the video using
the local features, for the purpose of face recognition. Local features which
are insensitive to noise, displacement, scale, rotation and illumination, are
extracted from arbitrary points on the images based on Speeded Up Robust
Feature (SURF) algorithm. The frames are then clustered using sequential
K-means algorithm. A representative frame from each cluster called the
key-frame is then determined for subsequent use in video based face recognition.
The proposed method has been demonstrated with experimental results obtained
using Honda/UCSD (name of a standard database available for face recognition
research) dataset 1.
Keywords: Feature extraction, frame clustering, key-frame, SURF,
unsupervised learning.
Received
December 12, 2013; accepted January 27, 2015