Face Searching in Large Databases


Both government agencies and private companies are investing significant resources to improve local/ remote access security. Badge or password-based procedures have proven to be too vulnerable, while biometric research has significantly grown, mostly due to technological progresses that allow using increasingly efficient techniques, yet at decreasing costs. Suitable devices capture images of user’s face, iris, etc., or other biometric elements such as fingerprints or voice. Each biometry calls for specific procedures. Measures from user’s data make up the so called biometric key, which is stored in a database (enrolment) or used for recognition (testing). During recognition, a subject’s key is matched against those in the database, producing a similarity score for each match. However, some drawbacks exist. For example, iris scanning is very reliable but presently too intrusive, while fingerprints are more socially accepted but not applicable to non-consentient people. On the other hand, face recognition represents a good solution even under less controlled conditions. In the last decade, many algorithms based on linear/non-linear methods, neural networks, wavelets, etc. have been proposed. Nevertheless, during Face Recognition Vendor Test 2002 most of them encountered problems outdoors. This lowers their reliability compared to other biometries, and underlines the need for more research. This chapter provides a survey of recent outcomes on the topic, addressing both 2D imagery and 3D models, to provide a starting reference to potential investigators. Tables containing different collections of parameters (such as input size, recognition rate, number of addressed problems) simplify comparisons. Some future directions are finally proposed. DOI: 10.4018/978-1-61520-991-0.ch002

Cite this paper

@inproceedings{Marsico2016FaceSI, title={Face Searching in Large Databases}, author={Maria De Marsico and Michele Nappi and Daniel Riccio and Sergio Vitulano}, year={2016} }