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Proceedings Paper

Face detection based on multiple kernel learning algorithm
Author(s): Bo Sun; Siming Cao; Jun He; Lejun Yu
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Paper Abstract

Face detection is important for face localization in face or facial expression recognition, etc. The basic idea is to determine whether there is a face in an image or not, and also its location, size. It can be seen as a binary classification problem, which can be well solved by support vector machine (SVM). Though SVM has strong model generalization ability, it has some limitations, which will be deeply analyzed in the paper. To access them, we study the principle and characteristics of the Multiple Kernel Learning (MKL) and propose a MKL-based face detection algorithm. In the paper, we describe the proposed algorithm in the interdisciplinary research perspective of machine learning and image processing. After analyzing the limitation of describing a face with a single feature, we apply several ones. To fuse them well, we try different kernel functions on different feature. By MKL method, the weight of each single function is determined. Thus, we obtain the face detection model, which is the kernel of the proposed method. Experiments on the public data set and real life face images are performed. We compare the performance of the proposed algorithm with the single kernel-single feature based algorithm and multiple kernels-single feature based algorithm. The effectiveness of the proposed algorithm is illustrated. Keywords: face detection, feature fusion, SVM, MKL

Paper Details

Date Published: 28 September 2016
PDF: 6 pages
Proc. SPIE 9971, Applications of Digital Image Processing XXXIX, 997134 (28 September 2016); doi: 10.1117/12.2235837
Show Author Affiliations
Bo Sun, Beijing Normal Univ. (China)
Siming Cao, Beijing Normal Univ. (China)
Jun He, Beijing Normal Univ. (China)
Lejun Yu, Beijing Normal Univ. (China)


Published in SPIE Proceedings Vol. 9971:
Applications of Digital Image Processing XXXIX
Andrew G. Tescher, Editor(s)

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