Smooth support vector machine for face recognition using principal component analysis
Mhd. Furqan, Abdullah Embong, Suryanti Awang, Santi Wulan Purnami, Sajadin Sembiring · Repository UIN Sumatera Utara (UIN Sumatera Utara) · 2009
Face is one of the unique features of human body.It has complicated characteristic.Facial features (eyes, nose, and mouth) can be used for face recognition in face detection.Support Vector Machine (SVM) is a new algorithm of data mining technique, recently received increasing popularity in machine learning community.The Smooth Support Vector Machine (SSVM) is a further development of a SVM.The SSVM convert the SVM primal formulation to a nonsmooth unconstrained minimization problem.Since the objective function of this unconstrained optimization problem is not twice differentiable, smoothing techniques will be used to solve this problem.This paper presents Smooth Support Vector Machines (SSVM) for few samples-based face recognition with Principal Component Analysis (PCA) for face extraction called eigenfaces.The Eigenfaces is projected onto human faces to identify features vector.This significant features vector can be used to identify an unknown face using the SSVM to construct a nonlinear classifier by using a nonlinear kernel for classification and recognition.Firstly, a preprocessing is done by taking facial features from various expressions from each individual.Secondly, we obtain the features vector from eigenfaces.Thirdly, we use SSVM to train and test dataset.The proposed system has shown competitive result and demonstrates that methods are available.