Image quality-based adaptive illumination normalisation for face recognition

Automatic face recognition is a challenging task due to intra-class variations. Changes in lighting conditions during enrolment and identification stages contribute significantly to these intra-class variations. A common approach to address the effects such of varying conditions is to pre-process the biometric samples in order normalise intra-class variations. Histogram equalisation is a widely used illumination normalisation technique in face recognition. However, a recent study has shown that applying histogram equalisation on well-lit face images could lead to a decrease in recognition accuracy. This paper presents a dynamic approach to illumination normalisation, based on face image quality. The quality of a given face image is measured in terms of its luminance distortion by comparing this image against a known reference face image. Histogram equalisation is applied to a probe image if its luminance distortion is higher than a predefined threshold. We tested the proposed adaptive illumination normalisation method on the widely used Extended Yale Face Database B. Identification results demonstrate that our adaptive normalisation produces better identification accuracy compared to the conventional approach where every image is normalised, irrespective of the lighting condition they were acquired

Illumination and Expression Invariant Face Recognition: Toward Sample Quality-based Adaptive Fusion

The performance of face recognition schemes is adversely affected as a result of significant to moderate variation in illumination, pose, and facial expressions. Most existing approaches to face recognition tend to deal with one of these problems by controlling the other conditions. Beside strong efficiency requirements, face recognition systems on constrained mobile devices and PDA's are expected to be robust against all variations in recording conditions that arise naturally as a result of the way such devices are used. Wavelet-based face recognition schemes have been shown to meet well the efficiency requirements. Wavelet transforms decompose face images into different frequency subbands at different scales, each giving rise to different representation of the face, and thereby providing the ingredients for a multi-stream approach to face recognition which stand a real chance of achieving acceptable level of robustness. This paper is concerned with the best fusion strategy for a multi-stream face recognition scheme. By investigating the robustness of different wavelet subbands against variation in lighting conditions and expressions, we shall demonstrate the shortcomings of current non-adaptive fusion strategies and argue for the need to develop an image quality based, intelligent, dynamic fusion strategy

Image-Quality-Based Adaptive Face Recognition

The accuracy of automated face recognition systems is greatly affected by intraclass variations between enrollment and identification stages. In particular, changes in lighting conditions is a major contributor to these variations. Common approaches to address the effects of varying lighting conditions include preprocessing face images to normalize intraclass variations and the use of illumination invariant face descriptors. Histogram equalization is a widely used technique in face recognition to normalize variations in illumination. However, normalizing well-lit face images could lead to a decrease in recognition accuracy. The multiresolution property of wavelet transforms is used in face recognition to extract facial feature descriptors at different scales and frequencies. The high-frequency wavelet subbands have shown to provide illumination-invariant face descriptors. However, the approximation wavelet subbands have shown to be a better feature representation for well-lit face images. Fusion of match scores from low- and high-frequency-based face representations have shown to improve recognition accuracy under varying lighting conditions. However, the selection of fusion parameters for different lighting conditions remains unsolved. Motivated by these observations, this paper presents adaptive approaches to face recognition to overcome the adverse effects of varying lighting conditions. Image quality, which is measured in terms of luminance distortion in comparison to a known reference image, will be used as the base for adapting the application of global and region illumination normalization procedures. Image quality is also used to adaptively select fusion parameters for wavelet-based multistream face recognition

Natural solution to antibiotic resistance: bacteriophages ‘The Living Drugs’

Antibiotics have been a panacea in animal husbandry as well as in human therapy for decades. The huge amount of antibiotics used to induce the growth and protect the health of farm animals has lead to the evolution of bacteria that are resistant to the drug’s effects. Today, many researchers are working with bacteriophages (phages) as an alternative to antibiotics in the control of pathogens for human therapy as well as prevention, biocontrol, and therapy in animal agriculture. Phage therapy and biocontrol have yet to fulfill their promise or potential, largely due to several key obstacles to their performance. Several suggestions are shared in order to point a direction for overcoming common obstacles in applied phage technology. The key to successful use of phages in modern scientific, farm, food processing and clinical applications is to understand the common obstacles as well as best practices and to develop answers that work in harmony with nature

Enhancing face recognition at a distance using super resolution

The characteristics of surveillance video generally include low-resolution images and blurred images. Decreases in image resolution lead to loss of high frequency facial components, which is expected to adversely affect recognition rates. Super resolution (SR) is a technique used to generate a higher resolution image from a given low-resolution, degraded image. Dictionary based super resolution pre-processing techniques have been developed to overcome the problem of low-resolution images in face recognition. However, super resolution reconstruction process, being ill-posed, and results in visual artifacts that can be visually distracting to humans and/or affect machine feature extraction and face recognition algorithms. In this paper, we investigate the impact of two existing super-resolution methods to reconstruct a high resolution from single/multiple low-resolution images on face recognition. We propose an alternative scheme that is based on dictionaries in high frequency wavelet subbands. The performance of the proposed method will be evaluated on databases of high and low-resolution images captured under different illumination conditions and at different distances. We shall demonstrate that the proposed approach at level 3 DWT decomposition has superior performance in comparison to the other super resolution methods

LBP based on multi wavelet sub-bands feature extraction used for face recognition

The strategy of extracting discriminant features from a face image is immensely important to accurate face recognition. This paper proposes a feature extraction algorithm based on wavelets and local binary patterns (LBPs). The proposed method decomposes a face image into multiple sub-bands of frequencies using wavelet transform. Each sub-band in the wavelet domain is divided into non-overlapping sub-regions. Then LBP histograms based on the traditional 8-neighbour sampling points are extracted from the approximation sub-band, whilst 4-neighbour sampling points are used to extract LBPHs from detail sub-bands. Finally, all LBPHs are concatenated into a single feature histogram to effectively represent the face image. Euclidean distance is used to measure the similarity of different feature histograms and the final recognition is performed by the nearest-neighbour classifier. The above strategy was tested on two publicly available face databases (Yale and ORL) using different scenarios and different combination of sub-bands. Results show that the proposed method outperforms the traditional LBP based features

Construction of dictionaries to reconstruct high-resolution images for face recognition

This paper presents an investigation into the construction of over-complete dictionaries to use in reconstructing a super resolution image from a single input low-resolution image for face recognition at a distance. The ultimate aim is to exploit the recently developed Compressive Sensing (CS) theory to develop scalable face recognition schemes that do not require training. Here we shall demonstrate that dictionaries that satisfy the Restricted Isometry Property (RIP) used for CS can achieve face recognition accuracy levels as good as those achieved by dictionaries that are learned from face image databases using elaborate procedures

In vitro Evaluation of the Antiviral Activity of an Extract of Date Palm (Phoenix dactylifera L.) Pits on a Pseudomonas Phage

A crude acetone extract of the pit of date palm (Phoenix dactylifera L.) was prepared and its antiviral activity evaluated against lytic Pseudomonas phage ATCC 14209-B1, using Pseudomonas aeruginosa ATCC 25668 as the host cell. The antiviral activity of date pits was found to be mediated by binding to the phage, with minimum inhibitory concentration (MIC) of <10 μg ml−1. The decimal reduction time (D-values), the concentration exponent (η) and the phage inactivation kinetics were determined. The date pit extracts show a strong ability to inhibit the infectivity of Pseudomonas phage ATCC 14209-B1 and completely prevented bacterial lysis, which it is hoped will promote research into its potential as a novel antiviral agent against pathogenic human viruses