Local descriptors and similarity measures for frontal face recognition: A comparative analysis

• A comparative analysis of the local descriptors for face recognition is given.
• A carefully structured taxonomy of the existing approaches is presented.
• Different similarity/dissimilarity measures are presented.
• Recent results on FERET database are presented.
• Future research directions are pointed out and discussed.

Face recognition based on local descriptors has been recently recognized as the state-of-the-art design framework for problems of facial identification and verification. Given the diversity of the existing approaches, the main objective of this paper is to present a comprehensive, in-depth comparative analysis of the recent face recognition methodologies based on local descriptors. We carefully review and contrast a suite of commonly encountered local descriptors. In particular, we highlight their main features in the setting of problems of facial recognition. The main advantages and limitations of the discussed methods are identified. Furthermore a carefully structured taxonomy of the existing approaches is presented We show that the presented techniques are particularly suitable for large scale facial authentication systems in which the training stage with the use of the overall face database might be computationally prohibited. A variety of approaches being used to realize a fusion of the local descriptions into the global ones are discussed along with their pros and cons. Furthermore different similarity measures and possible extensions and hybridizations with statistical learning techniques are elaborated on as well. Experimental results obtained for the FERET database are carefully assessed and compared.