NDLI: Augmenting SIFT with 3D Joint Differential Invariants for multimodal, hybrid face recognition

Content Provider	IEEE Xplore Digital Library
Author	Cadoni, M. Lagorio, A. Grosso, E.
Copyright Year	2013
Description	Author affiliation: Dept. of Political Sci., Commun., Eng. & Inf. Technol., Univ. of Sassary, Sassary, Italy (Cadoni, M.; Lagorio, A.; Grosso, E.)
Abstract	Hybrid face recognition methods combine holistic and feature based approaches with the aim of reaching a high level of efficiency and robustness. In this paper we propose a fully automatic algorithm for multimodal data consisting of 2D face images and their corresponding 3D scans. The algorithm is based on the extraction of simple image features using the Scale Invariant Feature Transform and the validation of the corresponding 3D key-points on the scans by means of Joint Differential Invariants based on local and global shape information. The 3D validation process goes through an optimisation procedure: first the invariants are generated from the 3D key-points, then a search for neighbour points that minimise the invariants distance is performed. The total number of matching invariants can be used as a measure of similarity between two faces. The efficacy of the method has been tested on the FRGCv2 database: in particular, experimental results demonstrate the significant contribution of 3D invariants in all cases characterised by a limited number of stable image features.
Starting Page	1
Ending Page	6
File Size	1105638
Page Count	6
File Format	PDF
ISBN	9781479905270
DOI	10.1109/BTAS.2013.6712746
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-29
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Three-dimensional displays Databases Shape Face recognition Feature extraction Face Probes
Content Type	Text
Resource Type	Article

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