Facial Expression Recognition: A Survey Nidhi N. Khatri #1 , Zankhana H. Shah #2 ,Samip A. Patel #3 #1 Computer Engineering Department, Gujarat Technological University Birla Vishvakarma Mahavidyalaya,, India #2,3 Information Technology Department, Gujarat Technological University Birla Vishvakarma Mahavidyalaya,, India Abstract- Face Expression plays an important role in human communication. Facial Expression Recognition (FER) is process performed by computers which consist of detect the face in the image and preprocess the face region, extracting facial expression features from image by analyzing the motion of facial features or change in the appearance of facial features and classifying this information into facial expression categories like prototypic facial expression such as fear, happy, sad or Action Units(AU) such as eye open or mouth stretched. Face Expression Recognition techniques have always been a very challenging task in real life applications because of the variations in the illumination, pose and occlusion. This paper presents a methodology for face expression recognition. Keywords: Facial Expression Recognition (FER), Feature Extraction, Feature Classification, Prototypical Facial Expression, Action Units (AU). I. INTRODUCTION Human face is a very useful and powerful source of communicative information about human behavior. It provides information about human personality, emotions and thoughts. Facial expression provides sensitive cues about emotional response and plays a major role in human interaction and nonverbal communications [2]. It can complement verbal communication, or can convey complete thoughts by itself. Researcher says that the verbal part or spoken words of a message contributes only for 7 percent to the effect of the message as a whole, the vocal part contributes for 38 percent, while facial expression of the speaker contributes for 55 percent to the effect of the spoken message. This implies that the facial expressions form the major modality in human communication. With the development of artificial intelligence and pattern recognition, people pay more and more attentions to facial expression recognition which is an important technology of intelligent human-interactive interface [3]. Different people may have different appearance for different expressions. But human can still recognize a wide range of different expressions. If we are not familiar with someone’s face we can recognize the person’s facial expression due to the universality of expressions. However it is a challenging task for a computer vision system to recognize an individual across different expressions or to classify the basic facial expression across different persons. Facial expression analysis has wide range of applications in areas such as in social psychology, video conferencing, user profiling, image retrieval, psychological area, face animation etc .Facial expressions help coordinate conversation, and have considerably more effect on whether a listener feels liked or disliked than the speaker's spoken words [1]. Facial expressions have been studied by cognitive psychologists, social psychologist, neurophysiologists, cognitive scientist and computer scientists. Computer vision based approaches to facial expression analysis discriminate among a small set of emotions. Challenge in face expression recognition system. It has already been stated that face expression recognition techniques have always been a very challenging task for researches because of all difficulties and limitations. Facial expression analysis and recognition is a complex task because faces vary from one person to another due to different age. The challenges associated with face expression recognition can be attributed to the following factors: Pose: The images of a face vary due to the relative camera- face position such as frontal or non-frontal. Face may have a different angle so some of facial features such as an eye or the nose may become partially or wholly occluded. To overcome this challenge implements good pre-processing techniques which are invariant to translation, rotation and scaling. As shown in fig 1 image which is used for feature extraction having different pose is complex. Fig 1: Different Pose in an image Occlusion: Faces may be partially occluded by other objects. In an image if face is occlude by some other faces or objects such as mask, hair, glasses as shown in fig 2. For that image extraction of expression features are complex. Fig 2: Occluded Face Nidhi N. Khatri et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (1) , 2014, 149-152 www.ijcsit.com 149