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Hand vein patterns are among the biometric traits being investigated today for identification purposes, attracting interest from both the research community and industry. A reliable and robust personal verification approach using dorsal hand vein patterns is presented in this paper. This approach needs less computational and memory requirements and has a higher recognition accuracy than similar methods. In our work, a near-infrared charge-coupled device camera is adopted as an input device for capturing dorsal hand vein images, due to its advantages of the low-cost and non-contact imaging. In the proposed approach, two finger-peaks are automatically selected to define the region of interest in the dorsal hand vein images. In order to obtain effective pattern of dorsal hand vein vascular, we proposed an innovative and robust adaptive Gabor filter method to extract the dorsal hand vein patterns and encode the vein features in bit string representation. The bit string representation, called VeinCode, offers speedy template matching and enables more effective template storage and retrieval. The similarity of two VeinCodes is measured by normalised Hamming distance. A total of 6160 dorsal hand vein images were collected from 308 persons to verify the validity of the proposed dorsal hand vein recognition approach. High accuracies (.99%) have been obtained by the proposed method, and the speed of the method (responding time ,0?8 s) is rapid enough for real-time recognition. Experimental results demonstrate that our proposed approach is feasible and effective for dorsal hand vein recognition.

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Underwater images often come with blurriness, lack of contrast, and low saturation due to the physics of light propagation, absorption, and scattering in seawater. To improve the visual quality of underwater images, many have proposed image processing methods that vary based on different approaches. We use a generative adversarial network (GAN)-based solution and generate high-quality underwater images equivalent to given raw underwater images by training our network to specify the differences between high-quality and raw underwater images. In our proposed method, which is called dilated GAN (DGAN), we add an additional loss function using structural similarity. Moreover, this method can not only determine the realness of the entire image but also functions with classification ability on each constituent pixel in the discriminator. Finally, using two different datasets, we compare the proposed model with other enhancement methods. We conduct several comparisons and demonstrate via full-reference and nonreference metrics that the proposed approach is able to simultaneously improve clarity and correct color and restores the visual quality of the images acquired in typical underwater scenarios.

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Neural style transfer (NST) is a technique based on the deep learning of a convolutional neural network (CNN) to create entertaining pictures by cleverly stylizing ordinary pictures with the predetermined visual art style. However, three issues must be carefully investigated during the generation of neural-stylized artwork: the color scheme, the strength of style of the strokes, and the adjustment of image contrast. To solve these problems and select image colorization based on personal preference, in this paper, we propose modified universal-style transfer (UST) method combined with the image fusion and color enhancement methods to design a good post-processing framework to tackle the three above-mentioned issues simultaneously. This work provides more visual effects for stylized images, and also can integrate into the UST method effectively. In addition, the proposed method is suitable for stylized images generated by any NST method, but it also works similarly to the Multi-Style Transfer (MST) method, which mixes two different stylized images. Finally, our proposed method successfully combined the modified UST method and post-processing method to meet personal preference.

Dorsal hand vein recognition is an emerging biometric technique researched today. In this paper, we propose a novel approach, the local feature-based ensemble 2-directional 2-dimensional linear discriminant analysis (LFBE(2D) 2 LDA), for dorsal hand vein recognition. The characteristic of the approach is to combine local and global information for vein recognition. First, we use block-based (2D) 2 PCA (B(2D) 2 PCA) to extract local feature from the dorsal hand vein image. Then, the global features are extracted by (2D) 2 LDA from the local feature-based ensemble to represent the vein image for classification. This method not only combines local and global feature, but also takes full advantage of the discriminant information and descriptive information of the images. The experiment result on our large dorsal hand vein database shows that high accuracies (98.55%) have been obtained by our proposed method. Keywords-dorsal hand vein recognition; block-based 2-directional 2-dimensional principal component analysis (B(2D)2PCA); 2-directional 2-dimensional linear discriminant analysis ((2D) 2LDA)