Face Recognition is a computer application that is capable of detecting, tracking, identifying or verifying human faces from an image or video captured using a digital camera. Although lot of progress has been made in domain of face detection and recognition for security, identification and attendance purpose, but still there are issues hindering the progress to reach or surpass human level accuracy. These issues are variations in human facial appearance such as; varying lighting condition, noise in face images, scale, pose etc. This research paper presents a new method using Local Binary Pattern (LBP) algorithm combined with advanced image processing techniques such as Contrast Adjustment, Bilateral Filter, Histogram Equalization and Image Blending to address some of the issues hampering face recognition accuracy so as to improve the LBP codes, thus improve the accuracy of the overall face recognition system. Our experiment results show that our method is very accurate, reliable and robust for face recognition system that can be practically implemented in real-life environment as an automatic attendance management system.

An improved face recognition algorithm and its application in attendance management system

A comprehensive review on deep learning based remote sensing image super-resolution methods

The red spectrum is saliently attenuated due to the absorption and scattering properties of water. The acquired underwater images show severe color cast in underwater scenes. In this letter, we propose a novel color correction method for underwater images, which removes color cast on single pixels based on scene depth. The experimental results demonstrate that our approach can significantly improve the color effect and provide a correct input for the subsequent underwater image defogging methods.

Auto Color Correction of Underwater Images Utilizing Depth Information

Providing security to the citizens is one of the most important and complex task for the governments around the world which they have to deal with Street crimes and theft are the biggest threats f

https://journals.sagepub.com/doi/pdf/10.1177/0020294020932344

Facial recognition system using LBPH face recognizer for anti-theft and surveillance application based on drone technology:

Underwater images require further enhancement to improve the image qualities caused by medium scattering and light absorption. Based on Contour Bougie (CB) morphology, we propose a new enhancement method to enhance the scene contours and improve the visibility of images captured underwater. Two structuring elements with different sizes are considered as the roving windows. Multiple morphological operations are designed for highlighting the rich details on the origin images. The enhanced images are normalized and stretched to improve the white balance of RGB channels. The comprehensive study of state-of-the-art algorithms is conducted to interpret the improvement of image quality by the proposed method. In addition, we use 890 raw underwater degraded images as the testing data. The quantitative and qualitative evaluations of these data demonstrate that the proposed method achieves better visible contrast for highlighting the details of the undersea creatures. The comparison with different underwater scenes proves that the proposed method improves the color balances of the degraded images.

An Underwater Image Vision Enhancement Algorithm Based on Contour Bougie Morphology

Facial recognition has always gone through a consistent research area due to its non-modelling nature and its diverse applications. As a result, day-to-day activities are increasingly being carried out electronically rather than in pencil and paper. Today, computer vision is a comprehensive field that deals with a high level of programming by feeding the input images/videos to automatically perform tasks such as detection, recognition and classification. Even with deep learning techniques, they are better than the normal human visual system. In this article, we developed a facial recognition system based on the Local Binary Pattern Histogram (LBPH) method to treat the real-time recognition of the human face in the low and high-level images. We aspire to maximize the variation that is relevant to facial expression and open edges so to sort of encode edges in a very cheap way. These highly successful features are called the Local Binary Pattern Histogram (LBPH).

/pdf/lbph-based-enhanced-real-time-face-recognition-4kda8jzusu.pdf

LBPH-based Enhanced Real-Time Face Recognition

Low-resolution medical images can seriously interfere with the medical diagnosis, and poor image quality can lead to loss of detailed information. Therefore, improving the quality of medical images and accelerating the reconstruction is of particular importance for diagnosis. To solve this problem, we propose a wavelet-based mini-grid network medical image super-resolution (WMSR) method, which is similar to the three-layer hidden-layer-based super-resolution convolutional neural network (SRCNN) method. Due to the amplification characteristics of wavelets, a stationary wavelet transform (SWT) is used instead of a discrete wavelet transform (DWT). Also, due to the nature of redundant (scale-by-scale) wavelets, it is possible to retain additional information about the image and restore high-resolution images in detail. For a large amount of training data, wavelet sub-band images, including approximation and frequency subbands are combined into a predefined full-scale factor. The mapping between the wavelet sub-band image and its approximate image is then determined. In order to ensure the reproducibility of the image, a method of adding a sub-pixel layer is proposed to realize the hidden layer, and replacing the small mini-grid-network on the hidden layer is of considerable significance to speed up the image recovery speed. Experimental results on the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) show that the model has better performance.

/pdf/wavelet-based-enhanced-medical-image-super-resolution-1t9lkx5e7d.pdf

Wavelet-Based Enhanced Medical Image Super Resolution

A remote-sensing image enhancement algorithm based on patch-wise dark channel prior and histogram equalisation with colour correction

The presence of suspended particles, such as fog, smoke, and dust, in the atmosphere reduces the quality of the captured image. It is essential to overcome these particles, because these particles have a very dire effect on different applications of image processing. We propose a new image dehazing method for remote sensing (RS) applications. Since the hazy RS image is affected by multiple colors and contrast reduction, our goal is to focus on degraded objects, including color correction and color–contrast enhancement. A “Piecewise Linear Transformation (PWLT)” is used to correct the color distortion, and then the color contrast is improved by applying the proposed method. The intensity distribution manipulation is one of the most common methods used to strengthen image contrast in the past. Compared with advanced techniques, the proposed method is easy to implement and suitable for real-time applications. Moreover, it is not necessary to require prior imaging conditions. The experimental results show that, in terms of subjective and visual quality, the color, contrast, naturalness, and high brightness of the object increase in the image to be improved.

Farah Deeba

Papers

LBPH-based Enhanced Real-Time Face Recognition

Wavelet-Based Enhanced Medical Image Super Resolution

A remote-sensing image enhancement algorithm based on patch-wise dark channel prior and histogram equalisation with colour correction

A Color Enhancement Scene Estimation Approach for Single Image Haze Removal

A deep hybrid neural network for single image dehazing via wavelet transform