Authors: Enrico Vezzetti, Federica Marcolin

Similarity Measures for Face Recognition

Personal Book: US $29 Special Offer (PDF + Printed Copy): US $56
Printed Copy: US $42
Library Book: US $116
ISBN: 978-1-68108-045-1
eISBN: 978-1-68108-044-4 (Online)
Year of Publication: 2015
DOI: 10.2174/97816810804441150101


Face recognition has several applications, including security, such as (authentication and identification of device users and criminal suspects), and in medicine (corrective surgery and diagnosis). Facial recognition programs rely on algorithms that can compare and compute the similarity between two sets of images.

This eBook explains some of the similarity measures used in facial recognition systems in a single volume. Readers will learn about various measures including Minkowski distances, Mahalanobis distances, Hansdorff distances, cosine-based distances, among other methods. The book also summarizes errors that may occur in face recognition methods.

Computer scientists "facing face" and looking to select and test different methods of computing similarities will benefit from this book. The book is also useful tool for students undertaking computer vision courses.


This book is a thorough organized treatise of the current knowledge on similarity measures applied to face recognition. Firstly, an overview on measures, distance functions and metrics is given. Then, each measure is introduced, defined, and inserted in the context of face recognition through a detailed summary of works in which the measure is applied to recognition. The works which employed the examined similarity measure are collected and reported chronologically, in order to have an overview on how the research changed over the time. After this part, each similarity measure is compared to others depending on the algorithms, recognition rate, and computational cost. Contributions that contain information about performances of these measures of similarity and compare them to others are reported. Lastly, some conclusions are drawn.


Declared None.

Conflict Of Interest

The authors confirm that this book contents have no conflict of interest.

Enrico Vezzetti
Federica Marcolin

Department of Management and Production Engineering
Politecnico di Torino
E-mails: and


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