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Abstract
We propose a multi-scale elastic graph matching (MS-EGM) algorithm for face detection, in which the conventional EGM is improved with two simple image processing techniques of the Gabor wavelet-based pyramid and the weak Gabor feature elimination. It is expected to solve difficulties of the real-time process in the conventional EGM. The Gabor wavelet-based pyramid effectively reduces not only the computational cost of the Gabor filtering but also the computational complexity of feature representation of a model face, preserving the facial information. The elimination of the weak Gabor feature extracted from an input image facilitates an accuracy of the Gabor feature similarity computations as unexpected. We then test that the MS-EGM can be capable of rapid face detection processing while achieving a high correct detection rate, comparable to the AdaBoost Haar-like (HL) feature cascade. We also show that the MS-EGM has strong robustness to the image of a face occluded with sunglasses and scarfs because of topologically preserved feature representations.
Highlights
Face detection is of importance in various research fields for computer vision technology
1.4 Discussion In this work, we have proposed the multi-scale elastic graph matching (MS-elastic graph matching (EGM)) that should be worthy of comparison to the detection ability of the AdaBoost HL cascade
In the future, introducing the rotation invariance [37,38] into our MS-EGM, we develop a more accurate face detection system that can detect the face when putting the head on one side
Summary
1.1 Introduction Face detection is of importance in various research fields for computer vision technology. There seem to exist several demerits as follows: (1) topological feature-based representations’ complexity and (2) the still difficult real-time process For the former, facial images are conventionally expressed with full Gabor feature representations encompassing many different orientations and many different spatial frequencies of the Gabor wavelet kernel [19]. 1.2.2 Gabor wavelet-based pyramid In the preprocess for the M facial image (Figure 2), we employ an image of the average face of German men created by a face generator [32]. We address that an additional merit in the Gabor wavelet-based pyramid is the less computational complexity for an image information preserved in feature representations
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