Current two dimensional face recognition methods rely on visible photometric or geometric attributes that are present in the intensity image. In many of these approaches a technique called Principal Component Analysis (PCA) is extensively used. PCA extracts the maximum intensity variations from the set of input images in the form of "eigen" faces which are used as a feature vector. In these approaches the intensity images used were mostly that of the subject's frontal face, which yielded promising results after doing PCA. These approaches however fail in the presence of facial expression, unstable lighting conditions and artifacts such as make-up, glasses etc. Thus, it is desirable to establish a new biometric source that will be least affected bythe afore mentioned factors. This study describes a face recognition method that is designed based on the consideration of anatomical and biomechanical characteristics of facial tissues. During facial expressions such as smile, frown, anger etc, various muscles get activated in tandem. A strain pattern inferred from a face expression can reveal an individual's signature associated with the underlying anatomical structure, and thus has the potential for face recognition. In this study, the strain is computed by measuring the displacement of a point on the face that results from a facial expression such as opening the mouth. The information provided by the change in the depth value for the face across the open and close mouth frames does not provide any information required for computing the strain maps, because the strain map depends on the relative displacements of two points on the face, which remains same with rigid motions of the face such as rotation and translation. Hence the information in the 2D spaceis sufficient to compute strain since the depth is assumed constant. The approach used to calculate strain computes the strain distribution directly using the mathematical definition of strain as the derivative of displacement in 2D space (XY plane). The strain values obtained are converted to gray scale intensity images, which are used as inputs for the intensity based PCA analysis. Experiments were conducted using 62 subjects. The data set comprised of two pairs of images for a subject: closed mouth and open mouth under bright and low light. Analysis of CMC and ROC curves indicate that the proposed strain map biometric is a promising new biometric that has the potential to improve the performance of current face recognition method. In summary, the contribution of this thesis is twofold: 1. Facial strain map proves to be promising new biometric. 2. Strain map helps increase the identification rate when used in conjunction with intensity based biometric as a multi-classifier.