1 A Comparative Study of Baseline Algorithms of Face Recognition Zahid Mahmood #1 , Tauseef Ali *2 , Shahid Khattak ┼3 , Samee U. Khan #4 # Department of Electrical & Computer Engineering, North Dakota State University, USA. 1 zahid.mahmood@ndsu.edu, 4 samee.khan@ndsu.edu * Faculty of Electrical Engineering, Mathematics, and Computer science, University of Twente, Netherlands. 2 t.ali@utwente.nl ┼ Department of Electrical Engineering, COMSATS Institute of Information Technology, Pakistan. 3 skhattak@ciit.net.pk Abstract—In this paper we present a comparative study of two well-known face recognition algorithms. The contribution of this work is to reveal the robustness of each FR algorithm with respect to various factors, such as variation in pose and low resolution of the images used for recognition. This evaluation is useful for practical applications where the types of the expected images are known. The two FR algorithms studied in this work are Principal Component Analysis (PCA) and AdaBoost with Linear Discriminant Analysis (LDA) as a weak learner. Images from multi-pie database are used for evaluation. Simulation results revealed that given one gallery (Training) face image and four different pose images as a probe (Testing), PCA based system is more accurate in recognizing pose, while AdaBoost was more robust on recognizing low resolution images. Key Words—AdaBoost, LDA, PCA. I. INTRODUCTION ACE Recognition (FR) is a phenomenon that humans usually do unconsciously. FR is defined as given an input face image of unknown subject and a database containing face images of known subjects, task is to determine the identity of the subject in the input image. Two basic FR scenarios are: (a) Identification and, (b) Verification. In Identification (1:N matching), a probe image of an unknown individual is identified by comparing the image with an image gallery of known individuals [1]. In Verification (1:1 matching), two images are compared with each other to conclude whether they originate from the same person [2]. Identification and Verification depend on various factors, such as change in facial expressions, appearance, aging, surgery, facial hairs, and changes in hairstyle. Moreover, occlusion, changes in scales, rotating faces in plan, variations in lighting/camera, and change in channel characteristics effect FR accuracy significantly [8]. FR has remained a challenging problem in image processing and computer vision [3]. Due to abrupt increase in crimes and terrorism in recent times, FR systems demand more attention in terms of accuracy and robustness when used in various domains, such as forensic applications. In such applications, the robustness of the system plays an important role [4]. Figure 1 explains general face verification/recognition procedure. First features are calculated in gallery images. These features are then compared with the features of the probe image and a similarity score is computed for a given comparison. Larger the similarity scores, the more similar images are in the given pair of images [22]. Matching Score Gallery Image Probe Image Face Detection Facial Features Computation Figure. 1. Face verification procedure The rest of the paper is organised as follows. Section II discusses the related work and highlights problem, this paper is focusing on. Section III briefly outlines the two face recognition algorithms that are compared. Simulation results are shown in Section IV. Finally, conclusions and future research directions are highlighted in Section V. II. RELATED WORK During the past three decades much effort has been done to improve the accuracy and robustness of the current FR systems. Researchers of [5] addressed phenomenon of FR using information in edges as independent components. They used Laplacian of Gaussian (LoG) and canny edges along with Principal Component Analysis (PCA) and Independent Component Analysis (ICA). This system suffered badly with the slight change in pose. Accuracy of this system was found to be 76.5%. The authors in [6] discussed FR using feed forward neural networks along with principal components. Results reported had 90% accuracy. The system had a drawback of being complex having high execution time. F