Article

Face Recognition by Exploring Information Jointly in Space, Scale and Orientation

Center for Biometrics and Security Research and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
IEEE Transactions on Image Processing (Impact Factor: 3.11). 01/2011; 20(1):247-56. DOI: 10.1109/TIP.2010.2060207
Source: PubMed

ABSTRACT Information jointly contained in image space, scale and orientation domains can provide rich important clues not seen in either individual of these domains. The position, spatial frequency and orientation selectivity properties are believed to have an important role in visual perception. This paper proposes a novel face representation and recognition approach by exploring information jointly in image space, scale and orientation domains. Specifically, the face image is first decomposed into different scale and orientation responses by convolving multiscale and multiorientation Gabor filters. Second, local binary pattern analysis is used to describe the neighboring relationship not only in image space, but also in different scale and orientation responses. This way, information from different domains is explored to give a good face representation for recognition. Discriminant classification is then performed based upon weighted histogram intersection or conditional mutual information with linear discriminant analysis techniques. Extensive experimental results on FERET, AR, and FRGC ver 2.0 databases show the significant advantages of the proposed method over the existing ones.

Download full-text

Full-text

Available from: Stan Z Li, Mar 25, 2014
0 Followers
 · 
158 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Weproposeanewlocaldescriptorfor fingerprint livenessdetection.Theinputimageisanalyzedbothin the spatialandinthefrequencydomain,inordertoextractinformationonthelocalamplitudecontrast, and onthelocalbehavioroftheimage,synthesizedbyconsideringthephaseofsomeselectedtransform coefficients. Thesetwopiecesofinformationareusedtogenerateabi-dimensionalcontrast-phase histogram,usedasfeaturevectorassociatedwiththeimage.Afteranappropriatefeatureselection,a trained linear-kernelSVMclassifier makesthe final live/fakedecision.Experimentsonthepublicly availableLivDet2011database,comprisingdatasetscollectedfromvarioussensors,provetheproposed method tooutperformthestate-of-the-artlivenessdetectiontechniques.
    Pattern Recognition 04/2015; DOI:10.1016/j.patcog.2014.05.021 · 2.58 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents an occlusion robust image representation method and apply it to face recognition. Inspired from the recent work [15], we propose a Gabor phase difference representation for occlusion robust face recognition. Based on the good ability of Gabor filters to capture image structure and the robustness to image occlusion shown in this paper, Gabor phase features are expected to be discriminative and robust for face representation in occlusion case. Besides, we adopt spectral regression based discriminant analysis with the extracted Gabor phase features to find the most discriminant subspace to classify different faces. In this way, an occlusion robust face image discriminant subspace is derived. Extensive experiments with various occlusion cases show the efficacy of the proposed method.
    Biometrics (ICB), 2012 5th IAPR International Conference on; 01/2012
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper proposes a classifier named ensemble of polyharmonic extreme learning machine, whose part weights are randomly assigned, and it is harmonic between the feedforward neural network and polynomial. The proposed classifier provides a method for human face recognition integrating fast discrete curvelet transform (FDCT) with 2-dimension principal component analysis (2DPCA). FDCT is taken to be a feature extractor to obtain facial features, and then these features are dimensionality reduced by 2DPCA to decrease the computational complexity before they are input to the classifier. Comparison experiments of the proposed method with some other state-of-the-art approaches for human face recognition have been carried out on five well-known face databases, and the experimental results show that the proposed method can achieve higher recognition rate.
    Neural Computing and Applications 05/2013; DOI:10.1007/s00521-013-1356-4 · 1.76 Impact Factor