Face Recognition Based on Adaptive Sparse Coefficient

Recently,robust sparse coding achieves high recognition rates on face recognition( FR),even when dealing with occluded images. However,robust sparse coding is that the coefficients are guaranteed global sparse when solving the sparse coefficients. In this paper,the coefficient vector is divided into multiple regions. Then,the elements in the object region are enabled to approximate global maximum by adding two constraint conditions( the maximal element of coefficient vector is in the object region; the sum of elements in the object region is the maximum value among all regions),which makes the distribution of sparse coefficient adapt to different classes of testing images. The efficacy of the proposed approach is verified on publicly available databases( i. e.,AR and Extended Yale B).Furthermore, the proposed method still can achieve a good performance when the training samples are limited.

Designing simultaneous multichannel receivers based on fast filter bank

A scheme to design a simultaneous multichannel receiver is proposed to process multichannel signals in parallel, which is achieved by exploiting the attractive characteristics of a fast filter bank（ FFB）, such as cascaded structure, high frequency selectivity and lowcomputational complexity. Based on the minimization of the objective function, quantified in terms of the total number of multiplications required, subject to prescribed allowable ripples in the passband and stopband, the impulse response coefficients of the prototype filter in each stage are obtained to meet the requirements of the overall specifications for each channel at the receiver side. Simulations and experimental results on the frequency modulation（ FM） broadcast mutlichannel signal receiving system with the FM range from88 to 108 MHz, built upon the proposed FFB structure, are performed to verify its performance. Those results indicate that the proposed scheme is efficient in FM audio indexing applications and has a lower computational complexity, which is approximately 66. 4% of the weighted overlap and add（ WOLA） filter banks based solution.

Sparse Representation over Shared Coefficients in Multispectral Pansharpening

The pansharpening process is for obtaining an enhanced image with both high spatial and high spectral resolutions by fusing a panchromatic（PAN） image and a low spatial resolution multispectral（MS） image. Sparse Principal Component Analysis（SPCA） method has been proposed as a pansharpening method, which utilizes sparse coefficients and over-complete dictionaries to represent the remote sensing data. However, this method still has some drawbacks, such as the existence of the block effect. In this paper, based on SPCA, we propose the Sparse over Shared Coefficients（SSC）, in which patches are extracted with a sliding distance of 1 pixel from a PAN image, and the MS image shares the sparse representation coefficients trained from the PAN image independently.The fused high-resolution MS image is reconstructed by K-SVD algorithm and iterations, and residual compensation is applied when the down-sampling constraint is not satisfied. The simulated experiment results demonstrate that the proposed SSC method outperforms SPCA and improves the overall effectiveness.