International Journal on Soft Computing ( IJSC ), Vol.2, No.2, May 2011 DOI : 10.5121/ijsc.2011.2201 1 MULTI-VIEW FACE DETECTION BASED ON KERNEL PRINCIPAL COMPONENT ANALYSIS AND KERNEL SUPPORT VECTOR TECHNIQUES Muzhir Shaban Al-Ani 1 and Alaa Sulaiman Al-Waisy 2 Department of Computer Science, College of Computer, Al-Anbar University, Iraq. 1 muzhir_shaban@yahoo.com 2 king_alaa87@yahoo.com ABSTRACT Detecting faces across multiple views is more challenging than in a frontal view. To address this problem, an efficient approach is presented in this paper using a kernel machine based approach for learning such nonlinear mappings to provide effective view-based representation for multi-view face detection. In this paper Kernel Principal Component Analysis (KPCA) is used to project data into the view-subspaces then computed as view-based features. Multi-view face detection is performed by classifying each input image into face or non-face class, by using a two class Kernel Support Vector Classifier (KSVC). Experimental results demonstrate successful face detection over a wide range of facial variation in color, illumination conditions, position, scale, orientation, 3D pose, and expression in images from several photo collections. KEYWORDS Face Detection, Face Recognition, Kernel Principal Component Analysis, Kernel Support Vector Machine. 1. INTRODUCTION Face detection is the first stage of an automated face recognition system, since a face has to be located in the overall image before it is recognized[1].As computers become faster and more affordable, many applications that use face detection/ localization are becoming an integral part of daily life. For example, face identification system, face tracking, video surveillance and security control system, and human computer interface. Those applications often require detected and segmented human face which is ready to be processed[2],[3]. However detecting a face under various environments is still challenging work. Some factors make face detection difficult. One is the variety of colored lighting sources; another is that facial features such as eyes may be partially or wholly occluded by a shadow generated by a bias lighting direction; and others are race and different face poses with/without glasses. Finally because faces are not rigid and have a high degree of variability in size, shape, color, and texture[4]. Therefore detection rate and the number of false positives are important factors in evaluating face detection systems[5]. This paper describes progress toward a system which can detect faces regardless of pose reliably and in real- time. In the presented system a kernel machine learning based approach for extracting nonlinear features of face images and using them for multi-view face detection. KPCA is applied on a set of view-labeled face images to learn nonlinear view- subspaces. Nonlinear features are the