DCTNet : A Simple Learning-free Approach for Face Recognition

PCANet was proposed as a lightweight deep learning network that mainly leverages Principal Component Analysis (PCA) to learn multistage filter banks followed by binarization and block-wise histograming. PCANet was shown worked surprisingly well in various image classification tasks. However, PCANet is data-dependence hence inflexible. In this paper, we proposed a data-independence network, dubbed DCTNet for face recognition in which we adopt Discrete Cosine Transform (DCT) as filter banks in place of PCA. This is motivated by the fact that 2D DCT basis is indeed a good approximation for high ranked eigenvectors of PCA. Both 2D DCT and PCA resemble a kind of modulated sine-wave patterns, which can be perceived as a bandpass filter bank. DCTNet is free from learning as 2D DCT bases can be computed in advance. Besides that, we also proposed an effective method to regulate the block-wise histogram feature vector of DCTNet for robustness. It is shown to provide surprising performance boost when the probe image is considerably different in appearance from the gallery image. We evaluate the performance of DCTNet extensively on a number of benchmark face databases and being able to achieve on par with or often better accuracy performance than PCANet.Comment: APSIPA ASC 201

Polygamy relations of multipartite systems

We investigate the polygamy relations of multipartite quantum states. General polygamy inequalities are given in the $\alpha$th $(\alpha\geq 2)$ power of concurrence of assistance, $\beta$th $(\beta \geq1)$ power of entanglement of assistance, and the squared convex-roof extended negativity of assistance (SCRENoA)

Robustness of a Blind Image Watermark Detector Designed by Orthogonal Projection

Blind digital watermarking, which can detect watermark without using the original image, is a key technique practical intellectual property protecting systems and concealment correspondence systems. In this paper, we discussed a blind detection method for the digital image watermark. The theories research show that the orthogonal projection sequence of a digital image is one-to-one correspondence with this digital image. To make use of this conclusion, we designed and realized a kind of blind watermark detector with the good performance. To calculate the correlation value between the image and watermark, the intensity information of digital image is not adopted, but the orthogonal projection sequence of this image is adopted. Experiment results show that this watermark detector not only to have very strong resistant ability to translation and rotation attacks, but also to have the good robustness to Gaussian noise. Performance of this watermark detector is better than general detector designed by making use of the intensity information directly. The conclusions obtained by experiments are useful to the research in the future