Face Recognition Template in Photo Indexing: A Proposal of Hybrid Principal Component Analysis and Triangular Approach (PCAaTA) L. G. Vu 1 , Abeer Alsadoon 1 , P.W.C. Prasad 1 , A. Monem 2 , A. Elchouemi 3 1 School of Computing and Mathematics, Charles Sturt University, Sydney, Australia 2 Computer Science Department, University of Technology, Baghdad, Iraq 3 Hewlett Packard Enterprise Abstract - Many features of human faces play important roles in designing a method that can perform face recognition. There are many current studies in the area of face recognition, but most of them have limitations. The Principal Component Analysis (PCA) is one of the best facial recognition algorithms. However, there are some noises that could affect the accuracy of this algorithm. The PCA works well with the supports of preprocessing steps such as illumination reduction, background removal and color conversion. Some current solutions have showed result in using combination of PCA and preprocessing steps. This paper proposes a hybrid solution in face recognition using PCA as the main algorithm with the support of triangular approach in face normalization in order to enhance the accuracy. To evaluate, the proposed approach is tested and the results are compared with the current solutions. Keywords: face recognition, face detection, photo indexing, Principal Component Analysis, triangular approach I. . INTRODUCTION With the rapid development of Information and Communication Technology (ICT), there has been a significant increase in the need of secure and convenient tools that can be used in real life. It is difficult to retrieve the large numbers of images and videos from mobile devices and private computers, therefore there is a strong need to use indexing methods. One of the possible solutions is face recognition that was developed by Woodrow W. Bledsoe in the 1960s. The idea is to determine features, such as mouths, noses, eyes and ears on images before calculating ratios and distances to a common point that is used to compare with reference data [1]. The problem with the current available solutions is the measurement and location progress, as they have to be done manually. Some studies have carried out those tasks automatically by applying many algorithms namely, Principal Components Analysis (PCA), Linear Discriminant Analysis (LDA) and Elastic Bunch Graph Matching (EBGM) [2]. However, those methods still have many limitations and need to be supported by a variety of algorithms and techniques in order to enhance the accuracy [3]. For example, one of the latest algorithms can detect multiple faces in a color image shows the accuracy of just 93% [4]. This paper aims to use an enhanced face recognition technique to identify an individual and index photos and videos. The paper is organized as follows: Section I is introduction, section II discusses the related work on the project topic, followed with impact factors in section III. The proposed method and are given in section IV. The results and discussion given in section VI followed by conclusion in the section 6 with possible future work. II. RELATED WORK A. Preprocessing Preprocessing is the first steps in face recognition which reduce noise affecting the human face images. Two main types of noises are illumination, and background. 1. Illumination reduction The negative effect of illumination on face detection is very clear. Illumination leads to shape of shadows, changes in highlights, shifts in the location and reversal of contrast gradients [5]. Therefore, reducing illumination effects is very important to enhance the accuracy rate. The color information is not used in identifying edges or other features [6]. Thus, color can be considered as noise and RGB (Red-Green-Blue) images should be converted into YCbCr (greyscale) images. Another illumination reduction method that can be considered as illumination-insensitive image integral normalized gradient image (INGI) [7]. 2. Human face cropping in largescale image The face detection and recognition can be affected in term of accuracy and time-consuming due to large scale of background. For instance, an image contains 90% of human face and 10% of background is more easy and informative to be analyzed, compared to an image contains 10% of human face and 90% of background. The color-based segmentation is based on the color of input image to differentiate the skin color region against non-skin color regions [4]. B. Face detection 1. Current face detection methods Face detection is aimed at detect and extract face feature from a given image. One of the most popular algorithms that has been used for a long time is the Viola and Jones algorithm [8]. This method is used to classify whether an image contains a human face and if so, where it is. The Viola-Jones algorithm 177 978-1-4673-9919-7/16/$31.00 ©2016 IEEE SSIAI 2016