Face Detection in Color Images

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Arjomand Inalou, Sania | 2009

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Type of Document: M.Sc. Thesis
Language: English
Document No: 40182 (55)
University: Sharif University of Technology, International Campus, Kish Island
Department: Science and Engineering
Advisor(s): Kasaei, Shohreh
Abstract:
Human face detection is an important research area with several applications such as human computer interface (HCI), face recognition, surveillance systems, security systems, and content-based image retrieval (CBIR). Face detection problem can be stated as “determining whether there are human faces in the image” and if there are “returning the location of each human face in the image” regardless of its position, size, scale, orientation, and lighting condition. In this thesis, we have proposed a new face detection method which combines the AdaBoost algorithm with skin color information and support vector machine (SVM). First, a cascade classifier based on AdaBoost is used to detect faces in images. As due to noise and illumination changes some non-faces might be detected too, we have used a skin color model in the YCbCr color space to remove some of the detected non-faces. Finally, we have utilized SVM in order to have minimal false alarm and extract faces more accurately. Experimental results show that the performance of the proposed method is higher than the basic AdaBoost in the sense of detecting fewer non-faces
Keywords:
Support Vector Machine (SVM) ; Color Space ; Skin Detection ; Adaboost Algorithm ; Face Detection

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