Book ChapterDOI
Face recognition by curvelet based feature extraction
Tanaya Mandal,Angshul Majumdar,Q. M. Jonathan Wu +2 more
- pp 806-817
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This paper proposes a new method for face recognition based on a multiresolution analysis tool called Digital Curvelet Transform, which takes the curvelet transforms of each of the original image and its quantized 4 bit and 2 bit representations to act as the feature set for classification.Abstract:
This paper proposes a new method for face recognition based on a multiresolution analysis tool called Digital Curvelet Transform. Multiresolution ideas notably the wavelet transform have been profusely employed for addressing the problem of face recognition. However, theoretical studies indicate, digital curvelet transform to be an even better method than wavelets. In this paper, the feature extraction has been done by taking the curvelet transforms of each of the original image and its quantized 4 bit and 2 bit representations. The curvelet coefficients thus obtained act as the feature set for classification. These three sets of coefficients from the three different versions of images are then used to train three Support Vector Machines. During testing, the results of the three SVMs are fused to determine the final classification. The experiments were carried out on three well known databases, viz., the Georgia Tech Face Database, AT&T "The Database of Faces" and the Essex Grimace Face Database.read more
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Journal ArticleDOI
Curvelet based face recognition via dimension reduction
TL;DR: The application of digital curvelet transform in conjunction with different dimensionality reduction tools, looking particularly at the problem of facial feature extraction from 2D images, shows that curvelets can serve as an effective alternative to wavelets as a feature extraction tool.
Journal ArticleDOI
Infrared face recognition: A comprehensive review of methodologies and databases
TL;DR: A comprehensive and timely review of the literature on this subject is presented in this article, where the authors present a summary of the inherent properties of infrared imaging which makes this modality promising in the context of face recognition.
Posted Content
Infrared face recognition: a comprehensive review of methodologies and databases
TL;DR: A summary of the inherent properties of infrared imaging which makes this modality promising in the context of face recognition, and a description of the main databases of infrared facial images available to the researcher.
Journal ArticleDOI
Facial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality.
TL;DR: A brief study of the various approaches and the techniques of emotion recognition is presented, including a succinct review of the databases that are considered as data sets for algorithms detecting the emotions by facial expressions.
Journal ArticleDOI
Extraction of illumination invariant facial features from a single image using nonsubsampled contourlet transform
TL;DR: This paper proposes to utilize the logarithmic nonsubsampled contourlet transform (LNSCT) to estimate the reflectance component from a single face image and refer it as the illumination invariant feature for face recognition, where NSCT is a fully shift-invariant, multi-scale, and multi-direction transform.
References
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The contourlet transform: an efficient directional multiresolution image representation
Minh N. Do,Martin Vetterli +1 more
TL;DR: A "true" two-dimensional transform that can capture the intrinsic geometrical structure that is key in visual information is pursued and it is shown that with parabolic scaling and sufficient directional vanishing moments, contourlets achieve the optimal approximation rate for piecewise smooth functions with discontinuities along twice continuously differentiable curves.
Journal ArticleDOI
Fast Discrete Curvelet Transforms
TL;DR: This paper describes two digital implementations of a new mathematical transform, namely, the second generation curvelet transform in two and three dimensions, based on unequally spaced fast Fourier transforms, while the second is based on the wrapping of specially selected Fourier samples.