  With the tremendous advancements in face recognition technology, face
modality has been widely recognized as a significant biometric identifier in
establishing a person's identity rather than any other biometric trait like
fingerprints that require contact sensors. However, due to inter-class
similarities and intra-class variations, face recognition systems generate
false match and false non-match errors respectively. Recent research focuses on
improving the robustness of extracted features and the pre-processing
algorithms to enhance recognition accuracy. Since face recognition has been
extensively used for several applications ranging from law enforcement to
surveillance systems, the accuracy and performance of face recognition must be
the finest. In this paper various face recognition systems are discussed and
analysed like RPRV, LWKPCA, SVM Model, LTrP based SPM and a deep learning
framework for recognising images from CCTV. All these face recognition methods,
their implementations and performance evaluations are compared to derive the
best outcome for future developmental works.
