Paper Info
Title
Mugshot database acquisition in video surveillance networks using incremental auto-clustering quality measures
Abstract
Face recognition has primarily focused on recognizing and matching face images against large, controlled databases of frontal views. Many of these techniques perform well against databases that have been collected from a reduced set of viewpoints, under controlled lighting, and are normalized for scale. Acquisition of these databases, however, particularly in unconstrained environments, remains a challenge. We present a real-time technique to acquire a mugshot database automatically from a video surveillance network. Mugshot extraction is a twofold problem. First, faces are detected and tracked in all cameras of the network. Face targets are analyzed to determine which frames represent actual mugshots capable of supporting subsequent matching and recognition. Next, mugshot candidates are evaluated based on their ability to improve the quality of the incrementally constructed database. We introduce a database quality measure, which assigns high value to mugshots of previously unseen subjects or mugshots that do not decrease separability of existing clusters. The quality measure is discounted for mugshots that are redundant or increase the intra-cluster spread. Results demonstrate that automatic acquisition of a high-quality database from a twelve-camera network is feasible. The quality of these databases is demonstrated using traditional methods to match faces accurately against the acquired database.
Year
DOI
Venue
2003
10.1109/AVSS.2003.1217921
AVSS
Keywords
Field
DocType
face detection,mugshot database acquisition,video signal processing,mugshot database,pattern clustering,incremental auto-clustering quality measures,face recognition,mugshot extraction,target tracking,face target,visual databases,acquired database,video surveillance networks,controlled databases,surveillance,database quality measure,optical tracking,quality measure,face target tracking,object detection,high-quality database,actual mugshots,matching face image,face image matching,real time,automatic control,image recognition
Data mining,Normalization (statistics),Viewpoints,Computer science,Artificial intelligence,Cluster analysis,Object detection,Facial recognition system,Computer vision,Three-dimensional face recognition,Pattern recognition,Video tracking,Optical tracking,Database
Conference
ISBN
Citations 
PageRank 
0-7695-1971-7
8
0.80
References 
Authors
16
2
Name
Order
Citations
PageRank
Quanren Xiong1141.37
Christopher Jaynes224520.92