A novel fuzzy rule base system for pose independent faces detection

Authors:
Payman Moallem;Bibi Somayeh Mousavi;S. Amirhassan Monadjemi
Affiliations:
Department of Electrical Engineering, Faculty of Engineering, University of Isfahan, HezarJerib Street, Isfahan, Iran;Department of Computer Engineering, Islamic-Azad University, and Member of Young Researchers Club, Zahedan Branch, Zahedan, Iran;Department of Computer Engineering, Faculty of Engineering, University of Isfahan, HezarJerib Street, Isfahan, Iran
Venue:
Applied Soft Computing
Year:
2011

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Abstract

Human face detection plays an important role in a wide range of applications such as face recognition, surveillance systems, video tracking applications, and image database management. In this paper, a novel fuzzy rule-based system for pose, size, and position independent face detection in color images is proposed. Subtractive clustering method is also applied to decide on the numbers of membership functions. In the proposed system, skin-color, lips position, face shape information and ear texture properties are the key parameters fed to the fuzzy rule-based classifier to extract face candidate in an image. Furthermore, the applied threshold on the face candidates is optimized by genetic algorithm. The proposed system consists of two main stages: the frontal/near frontal face detections and the profile face detection. In the first stage, skin and lips regions are identified in HSI color space, using fuzzy schemes, where the distances of each pixel color to skin-color and lips-color clusters are applied as the input and skin-likelihood and lips-like images are produced as the output. Then, the labeled skin and lips regions are presented to both frontal and profile face detection algorithms. A fuzzy rule-based containing the face and lips position data, along with the lips area and face shape are employed to extract the frontal/near frontal face regions. On the other hand, the profile face detection algorithm uses a geometric moments-based ear texture classification to verify its outcomes. The proposed method is tried on various databases, including HHI, Champion, Caltech, Bao, Essex and IMM databases. It shows about 98, 96 and 90% correct detection rates over 783 samples, in frontal, near frontal and profile face images, respectively.