Fig . 3 : The Two - Layer

Facial expressions play an important role in interpersonal relations as well as for security purposes. The malicious intensions of a thief can be recognized with the help of his gestures, facial expressions being its major part. This is because humans demonstrate and convey a lot of evident information visually rather than verbally. Although humans recognize facial expressions virtually without effort or delay, reliable expression recognition by machine remains a challenge. A picture portrays much more than its equivalent textual description. Along this theory, we assert that although verbal and gestural methods convey valuable information, facial expressions are unparalleled in this regard. In sustenance to this idea, a facial expression is considered to consist of deformations of facial components and their spatial relations, along with changes in the pigmentation of the same. This paper envisages the detection of faces, localization of features thus leading to emotion recognition in images. Key Terms: Facial Gestures, Action Units, Neural Networks, Fiducial Points, Feature Contours. INTRODUCTION Facial expression recognition is a basic process performed by every human every day. Each one of us analyses the expressions of the individuals we interact with, to understand best their response to us. The malicious intensions of a thief or a person tobe interviewed can be recognized with the help of his gestures, facial expressions being its major part. In this paper we have tried to highlight the facial expressions for security reasons. In the next step to Human-Computer interaction, we endeavor to empower the computer with this ability — to be able to discern the emotions depicted on a person‘s visage. This seemingly effortless task for us needs to be broken down into several parts for a computer to perform. For this purpose, we consider a facial expression to represent fundamentally, a deformation of the original features of the face. On a day-to-day basis, humans commonly recognize emotions by characteristic features displayed as part of a facial expression. For instance, happiness is undeniably associated with a smile, or an upward movement of the corners of the lips. This could be accompanied by upward movement of the cheeks and wrinkles directed outward from the outer corners of the eyes. Similarly, other emotions are characterized by other deformations typical to the particular expression. More often than not, emotions are depicted by subtle changes in some facial elements rather than their obvious contortion to represent its typical expression as is defined. In order to detect these slight variations induced, it is important to track fine-grained changes in the facial features. The general trend of comprehending observable components of facial gestures utilizes the FACS, which is also a commonly used psychological approach. This system, as described by Ekman , interprets facial information in terms of Action Units, which isolate localized changes in features such as eyes, lips, eyebrows and cheeks. The actual process is akin to a divide-andconquer approach, a step-by-step isolation of facial features, and then recombination of the interpretations of the same in order to finally arrive at a conclusion about the emotion depicted.