378 The International Arab Journal of Information Technology, Vol. 10, No. 4, July 2013 Analysis of Face Recognition under Varying Facial Expression: A Survey Marryam Murtaza, Muhammad Sharif, Mudassar Raza, and Jamal Hussain Shah Department of Computer Sciences, COMSATS Institute of Information Technology, Pakistan Abstract: Automatic face recognition is one of the most emphasizing dilemmas in diverse of potential relevance like in different surveillance systems, security systems, authentication or verification of individual like criminals etc. Adjoining of dynamic expression in face causes a broad range of discrepancies in recognition systems. Facial expression not only exposes the sensation or passion of any person but can also be used to judge his/her mental views and psychosomatic aspects. This paper is based on a complete survey of face recognition conducted under varying facial expressions. In order to analyze different techniques, motion-based, model-based and muscles-based approaches have been used in order to handle the facial expression and recognition catastrophe. The analysis has been completed by evaluating various existing algorithms while comparing their results in general. It also expands the scope for other researchers for answering the question of effectively dealing with such problems. Keywords: Facial expression, holistic, local, model, optical flow, muscles based and coding system. Received January 1, 2011; accepted May 24, 2011; published online August 5, 2012 1. Introduction Face recognition has the most relevance in real life issues of security, criminal investigation, and verification intention. Thus it has a broad range of applications. Three issues in the field of face recognition are: illumination variation [62], pose variation and more importantly expression variation which is the main focus of this paper. Facial expression is a way of non verbal communication. A person depicts his/her sentiment by using facial expression but these expressions create vagueness for recognition system. There is not been much research on this issue; and most of the researchers have investigated various algorithms to handle expression variation [32]. Generally, face is an amalgamation of bones, facial muscles and skin tissues [10]. When these muscles contract, deformed facial features are produced [23]. According to Chin and Kim [10] and Ekman and Friesen in [20] facial expression acts as a rapid signal that varies with contraction of facial features like eye brows, lips, eyes, cheeks etc., thereby affecting the recognition accuracy. On the other hand, static (skin color, gender, age etc.,) and slow signals (wrinkles, bulges) do not portray the type of emotion but do affect rapid signal. The work of facial expression basically started in nineteenth century. In 1872 Darwin [16], introduced an idea that there are definite inherent emotions that are derived from allied habits and are referred to as basic emotions. His idea was based on the assumption that the physiognomies are universal across ethnicities and customs which engross basic emotions like happiness, sadness, fear, disgust, surprise and anger. Primarily facial expressions are examined and analyzed by psychologists [23], but in 1978, Suwa et al. [65] were the first to attempt automatic face recognition using image sequence. Later the research on facial expression matured in 1990s (nineties) by the efforts of Mase and Pentland [48]. By the time, it had gained more attention due to its extensive applications in pertinent areas. Majority of the researchers focused on understanding image based techniques (video based), some focused on model based approaches, and some worked on motion based approaches, while some took advantages of facial expression recognition by proposing different algorithms that were incredibly practical in the field of medicine [18]. Generally, the attention on facial expression was focused to many social psychologists, clinical and medical practitioners, actors and artists etc., [4]. Later in the twentieth century facial expression became an active topic that was rigorously researched under the development of robotics, computer graphics, computer visions and animators etc., [4]. The brief survey conducted by Fasel and Luettin [23] and Rothkrantz [58] highlights different contributions to the research in this field from 1990 to 2001. The general frame work for automatic facial expression is shown in Figure 1. Primarily the face images are acquired and normalized in order to eliminate the complications like pose and illumination factor during face analysis. It is an axiom that feature extraction is a great milestone which uses various