Shoulie Xie

Skin heat transfer model of facial thermograms and its application in face recognition

It has been found that facial thermograms vary with ambient temperature, as well as other internal and external conditions, and result in severe decline in the facial recognition rate. To tackle this problem, a skin heat transfer (SHT) model based on thermal physiology is derived in this paper. The proposed model converts the facial thermograms into blood-perfusion data, which is revealed to reduce the within-class scatter of face images. The advantage of the derived blood-perfusion data over the raw thermograms for recognition is analyzed by the normalized reverse cumulative histogram. It is shown that blood-perfusion data are more consistent in representing facial features. The experiments conducted on both same-session and time-lapse data have further demonstrated that (1) the blood-perfusion data are less sensitive to ambient temperature, physiological and psychological conditions if the human bodies are in the steady state; (2) for time-lapse data, the performance with the blood-perfusion data is nearly identical to that of the same-session data, while the recognition rate with the temperature data dramatically decreases in this case. The major contributions of this work are the well-grounded infrared data preprocessing and the corresponding face recognition system.

Infrared face recognition by using blood perfusion data

This paper presents a blood perfusion model of human faces based on thermodynamics and thermal physiology. The target is to convert the facial temperature data which are liable to ambient temperature into consistent blood perfusion data in order to improve the performance of infrared (IR) face recognition. Our large number of experiments has demonstrated that the blood perfusion data are less sensitive to ambient temperature if the human bodies are in steady state, and the real data testing demonstrated that the performance by means of blood perfusion data is significantly superior to that via temperature data in terms of recognition rate.

Blind blur assessment for vision-based applications

In this paper, a criterion for objective defocus blur measurement is theoretically derived from one image. The essential idea is to estimate the point spread function (PSF) from the line spread function (LSF), whereas the LSF is constructed from edge information. It is proven that an edge point corresponds to the local maximal gradient in a blurred image, and therefore edges can be extracted from blurred images by conventional edge detectors. To achieve high accuracy, local Radon transform is implemented and a number of LSFs are extracted from each edge. The experimental results on a variety of synthetic and real blurred images validate the proposed method. The algorithm can be implemented for image quality evaluation in vision-based applications as no reference images are needed.

Movement detection for the synthesis of high dynamic range images

In this paper, we propose an intensity mapping function (IMF) based scheme to detect moving objects in a set of low dynamic range (LDR) images with different known exposure times. The objective is to remove ghosting artifacts from the eventual high dynamic range (HDR) image. Our contributions include a bidirectional similarity detection method, an adaptive threshold for movement detection, and an IMF based method for the synthesis of pixels to fill in the regions of moving objects. Experimental results show that the proposed scheme outperforms existing schemes.

A robust and fast anti-ghosting algorithm for high dynamic range imaging

This paper presents a robust and fast algorithm for automatically generating high dynamic range (HDR) images in presence of camera movement and moving objects. This scheme comprises five modules: 1) image alignment, 2) estimation of camera response function (CRF) in dynamic scenes, 3) moving object detection, 4) progressive image correction, and 5) construction of HDR images. The key advantage of the algorithm is the ability to generate HDR images without ghost artifact. The proposed algorithm is fast as it is a one-shot solution without iterative computation and post-processing or even manual operation. Experimental results demonstrate that the proposed method outperforms the existing commercial products.

Performance evaluation for quaternary DS-SSMA communications with complex signature sequences over Rayleigh-fading channels

Performance of quaternary direct-sequence spread-spectrum multiple-access (DS-SSMA) systems with complex signature sequences and complex modulators and receivers in flat Rayleigh fading is investigated in this paper. Due to the availability of potentially large sets of complex sequences with good correlation characteristics, the interest of using complex spreading sequences in DS-SSMA has increased dramatically. The complex spreading sequences investigated in this paper include the recently introduced orthogonal unified complex Hadamard transform (UCHT) sequences. In this paper, complex processing in modulators and receivers is also employed in order to take advantage of the correlation properties of complex signature sequences. The average bit error rate (BER) for quaternary synchronous systems is obtained first, and then the BER for quaternary asynchronous systems is evaluated using characteristic function approach. Result based on Gaussian approximation method is also presented for asynchronous systems. The numerical examples illustrate that the systems based on UCHT spreading sequences perform generally better than the Gold sequences and the 4-phase family A-sequences.

A robust method for detecting facial orientation in infrared images

This paper studies the problem of determining facial orientation without correspondences in distortion-related infrared images. To improve estimation accuracy and reduce sensitivity to noise and unavoidable error, a simple and robust method based on the single linkage clustering is proposed to simultaneously detect inlier set and estimate orientation angle under contaminated data. An iterative strategy is adopted to avoid random choice of link distance in the single linkage clustering. The experimental results indicate that the proposed method is substantially superior to the moment method. This method can also be extended to detect arbitrary objects with mirror symmetrical or nearly symmetrical property.

Wyner-Ziv Image Coding from Random Projections

In this paper, we present a Wyner-Ziv coding based on random projections for image compression with side information at the decoder. The proposed coder consists of random projections (RPs), nested scalar quantization (NSQ), and Slepian-Wolf coding (SWC). Most of natural images are compressible or sparse in the sense that they are well-approximated by a linear combination of a few coefficients taken from a known basis, e.g., FFT or Wavelet basis. Recent results show that it is surprisingly possible to reconstruct compressible signal to within very high accuracy from limited random projections by solving a simple convex optimization program. Nested quantization provides a practical scheme for lossy source coding with side information at the decoder to achieve further compression. SWC is lossless source coding with side information at the decoder. In this paper, ideal SWC is assumed, thus rates are conditional entropies of NSQ quantization indices. Recently theoretical analysis shows that for the quadratic Gaussian case and at high rate, NSQ with ideal SWC performs the same as conventional entropy-coded quantization with side information available at both the encoder and decoder. We note that the measurements of random projects for a natural large-size image can behave like Gaussian random variables because most of random measurement matrices behave like Gaussian ones if their sizes are large. Hence, by combining random projections with NSQ and SWC, the tradeoff between compression rate and distortion will be improved. Simulation results support the proposed joint codec design and demonstrate considerable performance of the proposed compression systems.

An alternating direction method for frame-based image deblurring with balanced regularization

In this paper, we propose an efficient algorithm for solving a balanced approach in frame-based image deblurring. The balanced approach is usually formulated as a minimization problem involving an ℓ2 data-fidelity term, an ℓ1 regularizer on sparsity of frame coefficients, and a penalty on distance of sparse frame coefficients to the canonical frame coefficients. The balanced approach bridges synthesis-based and analysis-based approaches. Our algorithm is based on a variable splitting strategy and the classical alternating direction method (ADM). This paper shows how the proposed algorithm can be applied to solve the balanced approach efficiently. More precisely, a regularized version of the Hessian matrix of the ℓ2 data-fidelity term is involved, and by exploiting fast tight frame and circular structure of the observation matrix, the matrix can perform efficiently for image deblurring application. Convergence of the proposed algorithm is guaranteed by the existing ADM theory. Numerical simulations illustrate the efficiency of our proposed algorithm in frame-based image deblurring.

Robust movement detection based on a new similarity index for HDR imaging

It is known that a high dynamic range (HDR) image can be produced by sequentially capturing a set of low dynamic range (LDR) images with different exposure times [Debevec and Malik 1997]. However, ghosting artifacts could be produced via this method when there are moving objects in a scene. In this poster, a similarity index is first introduced for such LDR images by using intensity mapping functions (IMFs) among them. The index is then applied to detect moving objects such that ghosting artifacts are removed from the eventual HDR image. The details are given as below.