DocumentCode :
3005754
Title :
Face detection using combination of Neural Network and Adaboost
Author :
Zakaria, Zulhadi ; Suandi, Shahrel A.
Author_Institution :
Sch. of Electr. & Electron. Eng., Intell. Biometric Group, Univ. Sains Malaysia, Nibong Tebal, Malaysia
fYear :
2011
fDate :
21-24 Nov. 2011
Firstpage :
335
Lastpage :
338
Abstract :
High false positive face detection is a crucial problem which leads to low performance face recognition in surveillance system. The performance can be increased by reducing these false positives so that non-face can be discarded first prior to recognition. This paper presents a combination of two well known algorithms, Adaboost and Neural Network, to detect face in static images which is able to reduce the false-positives drastically. This method utilizes Haar-like features to extract the face rapidly using integral image. A cascade Adaboost classifier is used to increase the face detection speed. Due to using only this cascade Adaboost produces high false-positives, neural network is used as the final classifier to verify face or non-face. For a faster processing time, hierarchical Neural Network is used to increase the face detection rate. Experiments on four different face databases, which consist more than one thousand images, have been conducted. Results reveal that the proposed method achieves about 93.34% of detection rate and 0.34% of false-positives compared to original cascade Adaboost method which achieves about 98.13% of detection rate with 6.50% of false-positives. The processed images size is 240 × 320 pixels. Each frame is processed at about 2.25 sec which is slightslower than the original method, which only takes about 0.82 sec.
Keywords :
Haar transforms; face recognition; feature extraction; image classification; learning (artificial intelligence); neural nets; video surveillance; Haar-like features; cascade Adaboost classifier; face databases; face extraction; face recognition; hierarchical neural network; high false positive face detection; integral image; static images; surveillance system; Computer architecture; Databases; Face; Face detection; Feature extraction; Humans; Training; Adaboost; Cascade; Face Detection; Neural Network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
TENCON 2011 - 2011 IEEE Region 10 Conference
Conference_Location :
Bali
ISSN :
2159-3442
Print_ISBN :
978-1-4577-0256-3
Type :
conf
DOI :
10.1109/TENCON.2011.6129120
Filename :
6129120
Link To Document :
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