Two-Dimensional Canonical Correlation Analysis of the Logically Concatenated Cross Binary Pattern for Cross Pose Face Recognition

Faceposerecognitionisoneofthechallengingareasincomputervision.Cross-posechangecauses thechangeintheinformationoffaceappearance.Themaximizationofintrasubjectcorrelationhelps towidentheintersubjectdifferenceswhichhelpsfurtherinachievingposeinvariance.Inthispaper, forcrossposerecognition,theauthorsproposetomaximizethecrossposecorrelationbyusingthe logically concatenatedcrossbinarypattern (LC-CBP)descriptor and twodimensional canonical correlationanalysis(2DCCA).TheLC-CBPdescriptorextractsthelocaltexturedetailsoffaceimages withlowcomputationcomplexityandthe2DCCAexplicitlysearchesforthemaximizationofthe correlatedfeaturestoretainmostinformativecontent.Jointfeatureconsiderationvia2DCCAhelps insettingupabettercorrespondencebetweenadiscretesetofnonfrontalposeandthefrontalpose ofthesamesubject.Experimentalresultsdemonstratethetwodimensionalcanonicalcorrelation LC-CBPdescriptoralongwithintensityvaluesimprovethecorrelation. KeywoRDS CCA (Canonical Correlation Analysis), Face Pose Recognition, LBP (Linear Binary Pattern), LC-CBP (Logically Concatenated Cross Binary Pattern)

[1]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[2]  Alejandro F. Frangi,et al.  Two-dimensional PCA: a new approach to appearance-based face representation and recognition , 2004 .

[3]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  John Shawe-Taylor,et al.  Canonical Correlation Analysis: An Overview with Application to Learning Methods , 2004, Neural Computation.

[5]  Dakshina Ranjan Kisku,et al.  An improved local pattern descriptor for biometrics face encoding: a LC–LBP approach toward face identification , 2017 .

[6]  Matti Pietikäinen,et al.  Local binary features for texture classification: Taxonomy and experimental study , 2017, Pattern Recognit..

[7]  Ming Li,et al.  2D-LDA: A statistical linear discriminant analysis for image matrix , 2005, Pattern Recognit. Lett..

[8]  H. Hotelling.  Relations Between Two Sets of Variates , 1936 .

[9]  Matti Pietikäinen,et al.  Median Robust Extended Local Binary Pattern for Texture Classification , 2016, IEEE Trans. Image Process..

[10]  C. V. Jawahar,et al.  Indian Movie Face Database: A benchmark for face recognition under wide variations , 2013, 2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG).

[11]  Wen Gao,et al.  Cross-pose Face Recognition by Canonical Correlation Analysis , 2015, ArXiv.

[12]  Harry Wechsler,et al.  The FERET database and evaluation procedure for face-recognition algorithms , 1998, Image Vis. Comput..

[13]  Jonghyun Choi,et al.  Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

[15]  G. Kukharev,et al.  Application of two-dimensional canonical correlation analysis for face image processing and recognition , 2010, Pattern Recognition and Image Analysis.