NDLI: Face recognition using early biologically inspired features

Content Provider	IEEE Xplore Digital Library
Author	Min Li Shenghua Bao Weihong Qian Zhong Su Ratha, N.K.
Copyright Year	2013
Description	Author affiliation: IBM China Res. Lab., China (Min Li; Shenghua Bao; Weihong Qian; Zhong Su) \|\| IBM Watson Res. Center, Yorktown Heights, NY, USA (Ratha, N.K.)
Abstract	Biologically inspired model (BIM) is proven to be an effective feature representation approach for visual object categorization. In BIM, two successive S(simple)-to-C(complex) hierarchical layers are performed to simulate the visual perception process of primate visual cortex. However, the intensive computational cost above C1 layer in BIM extremely limits its application in real-time object recognition tasks. This paper proposes to use a set of improved early biologically inspired features (EBIF, including S1 and C1) for face recognition, in which pyramidal statistics of mean and standard deviation rather than MAX pooling are used for scale-tolerant feature condensation and local normalization is performed on C1 layer. Incremental PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) are then combined to efficiently learn a discriminant subspace for feature dimensionality reduction. In the matching stage, Cosine similarity is adopted as the distance metric for a given face pair. Experimental results on two public face datasets and a mobile face dataset show the effectiveness of the proposed method.
Starting Page	1
Ending Page	6
File Size	616627
Page Count	6
File Format	PDF
ISBN	9781479905270
DOI	10.1109/BTAS.2013.6712711
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	Training Visualization Face recognition Mobile communication Face Principal component analysis
Content Type	Text
Resource Type	Article

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