Face detection using distribution-based distance and support vector machine

Peichung Shih, Chengjun Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - Sixth International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 2005
Pages327-332
Number of pages6
DOIs
StatePublished - Dec 1 2005
Event6th International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 2005 - Las Vegas, NV, United States
Duration: Aug 16 2005Aug 18 2005

Publication series

NameProceedings - Sixth International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 2005

Other

Other6th International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 2005
CountryUnited States
CityLas Vegas, NV
Period8/16/058/18/05

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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