C. JayaMohan et al./ Elixir Comp. Sci. & Engg. 55A (2013) 13251-13254 13251 Introduction FACE recognition has been studied for several decades. Comprehensive reviews of the related works can be found in [14], [21]. Even though the 2-D face recognition methods have been actively studied in the past, there are still some inherent problems to be resolved for practical applications. It was shown that the recognition rate can drop dramatically when the head pose and illumination variations are too large, or when the face images involve expression variations. Pose, illumination, and expression variations are three essential issues to be dealt with in the research of face recognition. To date, there was not much research effort on overcoming the expression variation problem in face recognition, though a number of algorithms have been proposed to overcome the pose and illumination variation problems. To improve the face recognition accuracy, researchers have applied different dimension reduction techniques, including principle component analysis (PCA) [3], linear discriminant analysis (LDA) [13], independent component analysis (ICA) [1], discriminant common vector (DCV) [2], kernal- PCA, kernal-LDA [5], kernal-DCV [10], etc. In addition, several learning techniques have been used to train the classifiers for face recognition, such as SVM. Although applying an appropriate dimension reduction algorithm or a robust classification technique may yield more accurate recognition results, they usually require multiple training images for each subject. However, multiple training images per subject may not be available in practice. This paper focuses mainly on the issue of robustness to expression and lighting variations. For example, a face Verification system for a portable device should be able to verify a client at any time (day or night) and in any place (indoors or outdoors). Traditional approaches for dealing with this issue can be broadly classified into three categories: appearance-based, normalization based, and feature-based methods. In direct appearance-based approaches, training examples are collected under different lighting conditions and directly (i.e. without undergoing any lighting preprocessing) used to learn a global model of the possible illumination variations. The other category is to use optical flow to compute the face warping transformation. Optical flow has been used in the task of expression recognition [4], [8]. However, it is difficult to learn the local motion in the feature space to determine the expression change for each face, since different persons have expressions in different motion styles. Martinez [15] proposed a weighting method that independently weighs the local areas which are less sensitive to expressional changes. The intensity variations due to expression may mislead the calculation of optical flow. A precise motion estimation method was proposed in [14], which can be further applied for expression recognition. However, the proposed motion estimation did not consider intensity changes due to different expressions. In this paper, we focus on the problem of face recognition from a single 2-D face image with facial expression. Note that this paper is not about facial expression recognition. For many practical face recognition problem settings, like using a passport photo for face identification at custom security or identifying a person from a photo on the ID card, it is infeasible to gather multiple training images for each subject, especially with different expressions. Therefore, our goal is to solve the expressive face recognition problem under the condition that the training database contains only neutral face images with one neutral face image per subject. In our previous work [11], we combined the advantages of the above two approaches: the unambiguous correspondence of feature point labeling and the flexible representation of optical flow computation. A constrained optical flow algorithm was proposed, which can deal with position movements and intensity changes at the same time when handling the corresponding feature. Algorithm, we can calculate the expressional motions from each neutral faces in the database to the input test image, and estimate the likelihood of such a facial expression movement. Tele: E-mail addresses: alphacse138@yahoo.com © 2013 Elixir All rights reserved Face recognition under expressions and lighting variations using artificial intelligence and image synthesizing C.JayaMohan, M.Saravana Deepak, M.L.Alphin Ezhil Manuel and D.C Joy Winnie Wise Department of CSE, Alpha College of Engineering, Chennai, T.N, India. ABSTRACT In this paper, we propose an integrated face recognition system that is robust against facial expressions by combining information from the computed intra-person optical flow and the synthesized face image in a probabilistic framework. Making recognition more reliable under uncontrolled lighting conditions is one of the most important challenges for practical face recognition systems. We tackle this by combining the strengths of robust illumination normalization. Our experimental results show that the proposed system improves the accuracy of face recognition from expressional face images and lighting variations. © 2013 Elixir All rights reserved. ARTICLE INFO Article history: Received: 12 September 2012; Received in revised form: 1 February 2013; Accepted: 19 February 2013; Keywords Face recognition, Constrained optical flow, Artificial intelligence, Synthesized image, Masked synthesized image. Elixir Comp. Sci. & Engg. 55A (2013) 13251-13254 Computer Science and Engineering Available online at www.elixirpublishers.com (Elixir International Journal)