POSSIBILITY FUZZY C-MEANS CLUSTERING FOR EXPRESSION INVARIANT FACE RECOGNITION

Face being the most natural method of identification for humans is one of the most significant biometric modalities and various methods to achieve efficient face recognition have been proposed. However the changes in face owing to different expressions, pose, makeup, illumination, age bring about marked variations in the facial image. These changes will inevitably occur and they can be controlled only till a certain degree beyond which they are bound to happen and will affect the face thereby adversely impacting the performance of any face recognition system. This paper proposes a strategy to improve the classification methodology in face recognition by using Possibility Fuzzy C-Means Clustering (PFCM). This clustering technique was used for face recognition due to its properties like outlier insensitivity which make it a suitable candidate for use in designing such robust applications.PFCM is a hybridization of Possibilistic C-Means (PCM) and Fuzzy C-Means (FCM) clustering algorithms. PFCM is a robust clustering technique and is especially significant for its noise insensitivity. It has also resolved the coincident clusters problem which is faced by other clustering techniques. Therefore the technique can also be used to increase the overall robustness of a face recognition system and thereby increase its invariance and make it a reliably usable biometric modality.