NDLI: Locality Preserving Discriminant Projection

Content Provider	IEEE Xplore Digital Library
Author	Shikkenawis, G. Mitra, S.K.
Copyright Year	2015
Description	Author affiliation: Dhirubhai Ambani Inst. of Inf. & Commun. Technol., Gandhinagar, India (Shikkenawis, G.; Mitra, S.K.)
Abstract	Face is the most powerful biometric as far as human recognition system is concerned which is not the case for machine vision. Face recognition by machine is yet incomplete due to adverse, unconstrained environment. Out of several attempts made in past few decades, subspace based methods appeared to be more accurate and robust. In the present proposal, a new subspace based method is developed. It preserves the local geometry of data points, here face images. In particular, it keeps the neighboring points which are from the same class close to each other and those from different classes far apart in the subspace. The first part can be seen as a variant of locality preserving projection (LPP) and the combination of both the parts is mentioned as locality preserving discriminant projection (LPDP). The performance of the proposed subspace based approach is compared with a few other contemporary approaches on some benchmark databases for face recognition. The current method seems to perform significantly better.
Starting Page	1
Ending Page	6
File Size	1260825
Page Count	6
File Format	PDF
ISBN	9781479919741
DOI	10.1109/ISBA.2015.7126365
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-03-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Databases Error analysis Face recognition Lighting Benchmark testing Face
Content Type	Text
Resource Type	Article

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