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Illumination invariant recognition and 3D reconstruction of faces using desktop optics

OPTICS EXPRESS 2011年第8期19卷 7491-7506页

作者： Mian, Ajmal Univ Western Australia Crawley WA 6009 Australia

We propose illumination invariant face recognition and 3D face reconstruction using desktop optics. The computer screen is used as a programmable extended light source to illuminate the face from different directions and acquire images. Features are extracted from these images and projected to multiple linear subspaces in an effort to preserve unique features rather than the most varying ones. Experiments were performed using our database of 4347 images (106 subjects), the extended Yale B and CMU-PIE databases and better results were achieved compared to the existing state-of-the-art. We also propose an efficient algorithm for reconstructing the 3D face models from three images under arbitrary illumination. The subspace coefficients of training faces are used as input patterns to train multiple Support Vector Machines (SVM) where the output labels are the subspace parameters of ground truth 3D face models. Support Vector Regression is used to learn multiple functions that map the input coefficients to the parameters of the 3D face. During testing, three images of an unknown/novel face under arbitrary illumination are used to estimate its 3D model. Quantitative results are presented using our database of 106 subjects and qualitative results are presented on the Yale B database. (C) 2011 Optical Society of America

关键词： Face recognition image recognition algorithms Laser scanning Light sources Reconstruction algorithms Three dimensional reconstruction

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Three dimensional object recognition with photon counting imagery in the presence of noise

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OPTICS EXPRESS 2010年第25期18卷 26450-26460页

作者： DaneshPanah, Mehdi Javidi, Bahram Watson, Edward A. Univ Connecticut Dept Elect & Comp Eng Storrs CT 06269 USA USAF Res Lab Sensors Directorate Wright Patterson AFB OH 45433 USA

Three dimensional (3D) imaging systems have been recently suggested for passive sensing and recognition of objects in photon-starved environments where only a few photons are emitted or reflected from the object. In this paradigm, it is important to make optimal use of limited information carried by photons. We present a statistical framework for 3D passive object recognition in presence of noise. Since in quantum-limited regime, detector dark noise is present, our approach takes into account the effect of noise on information bearing photons. The model is tested when background noise and dark noise sources are present for identifying a target in a 3D scene. It is shown that reliable object recognition is possible in photon-counting domain. The results suggest that with proper translation of physical characteristics of the imaging system into the information processing algorithms, photon-counting imagery can be used for object classification. (C) 2010 Optical Society of America

关键词： image processing algorithms image recognition algorithms Imaging systems Matched filtering Photon counting Three dimensional imaging

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Some practical issues in anomaly detection and exploitation of regions of interest in hyperspectral images

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APPLIED OPTICS 2006年第21期45卷 5223-5236页

作者： Goudail, Francois Roux, Nicolas Baarstad, Ivar Loke, Trond Kaspersen, Peter Alouini, Mehdi Normandin, Xavier Ecole Generaliste Ingenieurs Marseille Fresnel Inst Phys & Image Proc Grp UMR 6133 F-13397 Marseille 20 France Norsk Elektrooptikk AS N-1471 Lorenskog Norway Thales Res & Technol F-91767 Palaiseau France Thales Optron SA F-78283 Guyancourt France

We address method of detection of anomalies in hyperspectral images that consists in performing the detection when the spectral signatures of the targets are Unknown. We Show that, in real hyperspectral images. use of the full spectral resolution may not be necessary for detection but that the correlation properties of spectral fluctuations have to be taken into account in the design of the detection algorithm. Anomaly detectors are useful for detecting regions of interest (ROIs). but. as they are prone to false alarms. one must analyze the ROIs obtained further to decide whether they correspond to real targets. We propose a method of exploitation of these ROIs that consists in generating a single image in which the contrast of the ROI is optimized. (c) 2006 Optical Society of America.

关键词： Hyperspectral imaging image processing image recognition algorithms Point spread function Spatial resolution Spectral imaging

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Optical security system for the protection of personal identification information

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APPLIED OPTICS 2005年第5期44卷 742-750页

作者： Doh, YH Yoon, JS Choi, KH Alam, MS Jeju Natl Univ Dept Elect & Elect Engn Cheju 690756 South Korea SK Tech Co Gyunggi 572905 South Korea Jeju Natl Univ Dept Mech Engn Cheju 690756 South Korea Univ S Alabama Dept Elect & Comp Engn Mobile AL 36688 USA

A new optical security system for the protection of personal identification information is proposed. First, authentication of the encrypted personal information is carried out by primary recognition of a personal identification number (PIN) with the proposed multiplexed minimum average correlation energy phase-encrypted (MMACE_p) filter. The MMACE_p filter, synthesized with phase-encrypted training images, can increase the discrimination capability and prevent the leak of personal identification information. After the PIN is recognized, speedy authentication of personal information can be achieved through one-to-one optical correlation by means of the optical wavelet filter. The possibility of information counterfeiting can be significantly decreased with the double-identification process. Simulation results demonstrate the effectiveness of the proposed technique. (C) 2005 Optical Society of America.

关键词： Fourier transforms image encryption image processing image recognition algorithms Optical security Wavelet transforms

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Comparative study of face recognition techniques that use joint transform correlation and principal component analysis

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APPLIED OPTICS 2005年第5期44卷 688-692页

作者： Alsamman, A Alam, MS Univ New Orleans Dept Elect Engn New Orleans LA 70148 USA Univ S Alabama Dept Elect Engn Mobile AL 36688 USA

Face recognition based on principal component analysis (PCA) that uses eigenfaces is popular in face recognition markets. We present a comparison between various optoelectronic face recognition techniques and a PCA-based technique for face recognition. Computer simulations are used to study the effectiveness of the PCA-based technique, especially for facial images with a high level of distortion. Results are then compared with various distortion-invariant optoelectronic face recognition algorithms such as synthetic discriminant functions (SDF), projection-slice SDF, optical-correlator-based neural networks, and pose-estimation-based correlation. (C) 2005 Optical Society of America.

关键词： Face recognition image recognition algorithms Joint transform correlators Neural networks Optical neural systems Synthetic discrimination functions

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Noise robustness of nonlinear filters for image recognition

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JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS image SCIENCE AND VISION 2001年第9期18卷 2054-2071页

作者： Towghi, N Pan, LT Javidi, B Univ Connecticut Dept Elect & Syst Engn Storrs CT 06269 USA

We analyze the performance of the Fourier plane nonlinear filters in terms of signal-to-noise ratio (SNR). We obtain a range of nonlinearities for which SNR is robust to the variations in input-noise bandwidth. This is shown both by analytical estimates of the SNR for nonlinear filters and by experimental simulations. Specifically, we analyze the SNR when Fourier plane nonlinearity is applied to the input signal. Using the Karhunen-Loeve series expansion of the noise process, we obtain precise analytic expressions of the SNR for Fourier plane nonlinear filters in the presence of various types of additive-noise processes. We find a range of nonlinearities that need to be applied that keep the output SNR of the filter stable relative to changes in the noise bandwidth. (C) 2001 Optical Society of America.

关键词： Discrete Fourier transforms image recognition algorithms Matched filtering Nonlinear filtering Power spectra Spatial filtering

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Face verification through tracking facial features

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JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS image SCIENCE AND VISION 2001年第12期18卷 2969-2981页

作者： Li, BX Chellappa, R Sharp Labs Amer Camas WA 98607 USA Univ Maryland Ctr Automat Res College Pk MD 20742 USA

We propose an algorithm for face verification through tracking facial features by using sequential importance sampling. Specifically, we first formulate tracking as a Bayesian inference problem and propose to use Man kov chain Monte Carlo techniques for obtaining an empirical solution. A reparameterization is introduced under parametric motion assumption, which facilitates the empirical estimation and also allows verification to be addressed along with tracking. The facial features to be tracked are defined on a grid with Gabor attributes (jets). The motion of facial feature points is modeled as a global two-dimensional (2-D) affine transformation (accounting for head motion) plus a local deformation (accounting for residual motion that is due to inaccuracies in 2-D affine modeling and other factors such as facial expression). Motion of both types is processed simultaneously by the tracker: The global motion is estimated by importance sampling, and the residual motion is handled by incorporating local deformation into the measurement likelihood in computing the weight of a sample. Experiments with a real database of face image sequences are presented. (C) 2001 Optical Society of America.

关键词： Face recognition image recognition algorithms Machine vision Motion estimation Real time implementation Segmentation

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Ip-norm optimum filters for image recognition. Part I. algorithms

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Journal of the Optical Society of America A 1999年第8期16卷 1928-1935页

作者： Nasser Towghi Bahram Javidi Department of Electrical and System Engineering University of Connecticut U-157 Storrs Connecticut 06269-3357

Ordinarily, filters are derived from the optimization of certain expressions with respect to the mean squared metric. We construct a family of linear and nonlinear processors (filters) for image recognition that is l p -norm optimum in terms of tolerance to input noise and discrimination capabilities. The l p norm is the generalization of the usual mean squared ( l 2 ) norm, which we obtain by replacing the exponent 2 with any positive constant p (usually p ≥ 1 ) . These processors are developed by minimizing the l p norm of the filter output that is due to the input scene and the output that is due to input noise. We use the l p norm to measure the size of the filter output that is due to noise so that we can obtain greater freedom in adjusting the noise robustness and discrimination capabilities. We give a unified theoretical basis for developing these filters. This family of filters includes some of the existing linear and nonlinear filters, giving us a subfamilies of processors, which we denote by H q σ and H q . The values of q control the discrimination capabilities and the robustness of the processors. The parameter σ is the standard deviation of the noise process.

关键词： image recognition image recognition algorithms Joint transform correlators Linear filtering Matched filtering Phase only filters

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Ip-norm optimum filters for image recognition. Part II: performance evaluation

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Journal of the Optical Society of America A 1999年第9期16卷 2146-2150页

作者： Bahram Javidi Nasser Towghi Department of Electrical and System Engineering University of Connecticut U-157 Storrs Connecticut 06269-3357

We examined the performance of linear and nonlinear processors (filters) for image recognition that are l p -norm optimum in terms of tolerance to input noise and discrimination capabilities. These processors were developed by minimizing the l p norm of the filter output due to the input scene and the output due to the noise. We tested the performance of the l p -norm optimum filters by measuring the average peak-to-sidelobe ratio of the output of the filters for different values of p . We also tested the performance of these filters by placing a target in a scene containing additive noise and a realistic background. For the images presented here, the filters detected the target in the presence of additive noise and a realistic background. The tests conducted show that the discrimination capabilities of the l p -norm filters improve as p decreases ( p > 1 ) . This is shown by sharper peaks at the target location and higher average peak-to-sidelobe ratios for smaller values of p .

关键词： algorithms Fourier transforms image recognition image recognition algorithms Linear filtering Matched filtering

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Optoelectronic region of interest detection: an application in automated cytology

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APPLIED OPTICS 1998年第25期37卷 6011-6025页

作者： Narayanswamy, R Johnson, KM Univ Colorado Ctr Optoelect Comp Syst Boulder CO 80309 USA

Diagnostic cytology, which is used to screen for cervical cancer, involves characterizing cellular features such as shape, size, and texture. Automated screening of cervical smear slides is desirable but computationally challenging since each slide requires processing 2 x 10(9) pixels at a resolution of 0.8 mu m per pixel. We demonstrate that the throughput of optical processors can be exploited in automated cervical smear-screening systems. In particular, we identify a morphological shape detector to perform the initial region of interest (ROT) detection and to demonstrate experimentally its optoelectronic implementation. The ROI detector is tested on 200 images, and its performance is characterized as a receiver operating characteristic (ROC). The area under the ROC curve is as high as 96.4% of the total area. The simulation and the experimental results are found comparable, and the discrepancy between the two results is determined to be a function of the number of bits represented in the filter plane device. (C) 1998 Optical Society of America.

关键词： Binary phase only filters Diode lasers image processing image recognition algorithms image sharpening Optical processing devices

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