Informatics Publishing Limited

Abstract

Face recognition is one of the challenging applications of image processing. It has been actively investigated by the scientific community and has already taken its place in modern consumer software. However, there are still major challenges remaining e.g. variations in pose, lighting and appearance, even with well known face recognition systems. In this paper, we present a Robust face recognition system should posses the ability to recognize identity despite many variations in pose, lighting and appearance. This proposed face recognition system consists of a novel illumination-insensitive preprocessing method, a combined method of feature extraction using discrete wavelet Transform and hybrid fourier transform with multiple face model to improve the recognition rate, and a score fusion scheme. First, in the preprocessing stage, a face image is transformed into an illuminationinsensitivimage, called an ―integral normalized gradient image,‖ by normalizing and integrating the smoothed gradients of a facial image.Then, for feature extraction of complementary classifiers,multipleface models based upon discrete wavelet and hybrid fourier featuresare applied. The wavelet featuresare extracted from wavelet coefficient values with the same size as the original image. Thesecoefficients are used to describe the face image. The hybrid Fouriereatures are extracted from different Fourier domains in differentfrequency bandwidths and then each feature is individually classified by linear discriminant analysis. In addition, multiple face models are enerated by plural normalized face images that have different eye distances. Finally, to combine scores from multiple complementarym classifiers, a log likelihood ratio-based score fusion scheme is applied.

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Abstract

Conventional Robust Reversible Watermarking methods have limited robustness in extracting watermarks from the watermarked images destroyed by different unintentional attacks and some of them suffer from extremely poor invisibility for water marked images. It is necessary to have a framework to address these three problems and further improve its performance. It presents a novel practical structure, wavelet-area arithmetical quantity histogram shifting and cluster (WSQH-SC). Compared with conservative methods, WSQH-SC resourcefully constructs new watermark  in addition to removal procedures by histogram shifting and clustering, which are important for civilizing robustness and reducing run-time difficulty in addition, WSQH-SC includes the property-inspired pixel adjustment to effectively handle overflow and underflow of pixels. This results in acceptable reversibility and invisibility. To increase its practical applicability WSQH-SC designs an enhanced pixel-wise masking to balance robustness and invisibility. It perform extensive experiments over normal, medicinal and artificial opening radar imagery to show the effectiveness of WSQH-SC by comparing with the histogram rotation-based and histogram distribution constrained methods.

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Abstract

Automatic license plate recognition is a Computer Vision technique which is able to recognize a license plate number. This system is useful in many field likes parking lots, private and public entrances, theft control. In this paper we designed such a system. First we capture the image from camera then load into system after that we used Open CV library tools. Then we make the training set of different characters of different sizes. On the basis of these training set we extracted the character from images. When the license plate is detected, its digits are recognized and displayed in the GUI. In this mainly focuses on Neural Network and proprietary tools Open CV using python.

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