This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Sixth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'05)
Face Detection Using Distribution-Based Distance and Support Vector Machine
Las Vegas, Nevada
August 16-August 18
ISBN: 0-7695-2358-7
Peichung Shih, New Jersey Institute of Technology
Chengjun Liu, New Jersey Institute of Technology
This paper presents a novel face detection method by applying distribution-based distance (DBD) measure and Support Vector Machine (SVM). The novelty of our DBD-SVM method comes from the integration of discriminating feature analysis, face class modeling, and support vector machine for face detection. First, the discriminating feature vector is defined by combining the input image, its 1-D Haar wavelet representation, and its amplitude projections. Then the DBD-SVM method statistically models the face class by applying the discriminating feature vectors and defines the distribution-based distance measure. Finally, based on DBD and SVM, three classification rules are applied to separate faces and nonfaces. Experiments using images from the MIT-CMU test sets show the feasibility of our new face detection method. In particular, when using 92 images (containing 282 faces) from the MIT-CMU test sets, our DBD-SVM method achieves 98.2% correct face detection accuracy with 2 false detections, a performance comparable to the state-of-the-art face detection methods, such as the Schneiderman-Kanade?s method.
Citation:
Peichung Shih, Chengjun Liu, "Face Detection Using Distribution-Based Distance and Support Vector Machine," iccima, pp.327-332, Sixth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'05), 2005
Usage of this product signifies your acceptance of the Terms of Use.