As computerised storage and control of information is now a reality, it is increasingly necessary that personal identity verification be used as the automated method of access control to this information. Automatic facial recognition is now being viewed as an ideal solution to the problem of unobtrusive, high security, personal identity verification. However, few researchers have yet managed to produce a face recognition algorithm capable of performing successful recognition, without requiring substantial data storage for the personal information. This thesis reports the development of a feature and measurement based system of facial recognition, capable of storing the intrinsics of a facial image in a very small amount of data. The parameterisation of the face into its component characteristics is essential to both human and automated face recognition. Psychological and behavioural research has been reviewed in this thesis in an attempt to establish any key pointers, in human recognition, which can be exploited for use in an automated system. A number of different methods of automated facial recognition which perform facial parameterisation in a variety of different ways are discussed. In order to store the relevant characteristics and measurements about the face, the pertinent facial features must be precisely located from within the image data. A novel technique of Limited Feature Embedding, which locates the primary facial features with a minimum of computational load, has been successfully designed and implemented. The location process has been extended to isolate a number of other facial features. With regard to the earlier review, a new method of facial parameterisation has been devised. Incorporated in this feature set are local feature data and structural measurement information about the face. A probabilistic method of inter-person comparisons which facilitates recognition even in the presence of expressional and temporal changes, has been successfully implemented. Comprehensive results of this novel recognition technique are presented for a variety of different operating conditions.