نورا محمد صاحب الكرعاوي

After decades of research, automatic facial expression recognition (AFER) has been shown to work well when restricted to subjects with a limited range of ages. Expression recognition in subjects having a large range of ages is harder as it has been shown that ageing, health, and lifestyle affect facial expression. In this paper, we present a discriminative system that explicitly predicts expression across a large range of ages, which we show to perform better than an equivalent system which ignores age. In our system, we first build a fully automatic facial feature point detector (FFPD) using random forest regression voting in a constrained local mode (RFRV-CLM) framework (Cootes et al., in: European conference on computer vision, Springer, Berlin, 2012) which we use to automatically detect the location of key facial points, study the effect of ageing on the accuracy of point localization task. Second, a set of age group estimator and age-specific expression recognizers are trained from the extracted features that include shape, texture, appearance and a fusion of shape with texture, to analyse the effect of ageing on the face features and subsequently on the performance of AFER. We then propose a simple and effective method to recognize the expression across a large range of ages through using a weighted combination rule of a set of age group estimator and age specific expression recognizers (one for each age group), where the age information is used as prior knowledge to the expression classification. The advantage of using the weighted combination of all the classifiers is that more information about the classification can be obtained and subjects whose apparent age puts them in the wrong chronological age group will be dealt with more effectively. The performance of the proposed system was evaluated using three age-expression databases of static and dynamic images for deliberate and spontaneous expressions: FACES (Ebner et al., in Behav Res Methods 42:351–362, 2010) (2052 images), Lifespan (Minear and Park in Behav Res Methods Instrum Comput 36:630–633, 2004) (844 images) and NEMO (Dibeklioğlu et al., in: European conference on computer vision, Springer, Berlin, 2012) (1,243 videos). The results show the system to be accurate and robust against a wide variety of expressions and the age of the subject. Evaluation of point localization, age group estimation and expression recognition against ground truth data was obtained and compared with the existing results of alternative approaches tested on the same data. The quantitative results with 2.1% error rates (using manual points) and 3.0% error rates (fully automatic) of expression classification demonstrated that the results of our novel system were encouraging in comparison with the state-of-the-art systems which ignore age and alternative models recently applied to the problem.

This paper proposes to combine both the texture and deep features to build a robust diabetic foot ulcer recognition system since both features represent valuable information about the disease. The proposed system consists of three stages: feature extraction, feature fusion, and DFU classification. The feature extraction is performed by extracting the handcrafted and deep features. The feature fusion is performed by concatenating both feature vectors into a single vector. The DFU classification is performed by training a random forest classifier on the fusion vectors and the resulting classifier is used then for classification. Experimental results showed that the proposed approach provides satisfactory performance in DFU, ischaemia, and infection classification.

Diabetic foot ulcer (DFU) is a major complication of diabetes and can lead to lower limb amputation if not treated early and properly. In addition to the traditional clinical approaches, in recent years, research on automation using computer vision and machine learning methods plays an important role in DFU classification, achieving promising successes. The most recent automatic approaches to DFU classification are based on convolutional neural networks (CNNs), using solely RGB images as input. In this paper, we present a CNN-based DFU classification method in which we showed that feeding an appropriate feature (texture information) to the CNN model provides a complementary performance to the standard RGB-based deep models of the DFU classification task, and better performance can be obtained if both RGB images and their texture features are combined and used as input to the CNN. To this end, the proposed method consists of two main stages. The first stage extracts texture information from the RGB image using the mapped binary patterns technique. The obtained mapped image is used to aid the second stage in recognizing DFU as it contains texture information of ulcer. The stack of RGB and mapped binary patterns images are fed to the CNN as a tensor input or as a fused image, which is a linear combination of RGB and mapped binary patterns images. The performance of the proposed approach was evaluated using two recently published DFU datasets: the Part-A dataset of healthy and unhealthy (DFU) cases [17] and Part-B dataset of ischaemia and infection cases [18]. The results showed that the proposed methods provided better performance than the state-of-the-art CNN-based methods with 0.981% (AUC) and 0.952% (F-Measure) on the Part-A dataset, 0.995% (AUC) and 0.990% (F-measure) for the Part-B ischaemia dataset, and 0.820% (AUC) and 0.744% (F-measure) on the Part-B infection dataset.

In the modern-day, Diabetic disease is one of the most chronic and appalling diseases humanity faces. There are 463 million people had Diabetes worldwide, and it caused approximately 4.2 million deaths, according to the International Diabetes Federation (IDF) Diabetes Atlas Ninth edition 2019. Therefore diabetic patients need state-of-the-art healthcare against such diseases and propose early prediction to help decrease the risks related to such diseases. In this context, this research, a diabetes diagnosis system, has proposed to analyze two different diabetes datasets, namely PIMA Indian Diabetes and data of Dr. John Schorling. Linear Discriminant Analysis (LDA) and Genetic algorithm (GA) methods used for feature selection and four techniques implemented to evaluate the classification are Bagging algorithm, Random forest, Logistic Model Tree (LMT), and JRip algorithm. The results have shown that a random forest classifier using LDA and GA obtained better accuracy (90.89%) in DatasetI. At the same time, DatasetII is better than GA in Random forest, random forest-LDA, JRip-LDA classifiers (91.44%).

This paper presents a new face descriptor based on binary robust independent elementary features (BRIEF) (Calonder et al. in IEEE Trans Pattern Anal Mach Intell 34(7):1281–1298, 2012; in: European conference on computer vision, Springer, pp 778–792, 2010). The most important properties of BRIEF are the independence of the descriptor length from the matching window size and the possibility of using a subsample of pixel pairs located at arbitrary positions in the matching window. Furthermore, BRIEF is computationally simple and gives a compact representation. The BRIEF descriptor can be used to generate discriminative features globally from an image. However, when BRIEF is used to generate features from a region of an image with no explicit shape such as a face in an image, BRIEF must be used locally to ensure that each pixel in the region is evaluated locally to capture the local properties. In this paper, the performance of BRIEF feature is evaluated in the task of AFER. Using three different facial expression databases, we demonstrate that BRIEF provides satisfactory, encouraging and comparable performance to the performance of alternative face representation tested on the same data, and it is efficient in terms of memory usage and execution time.

After decades of research, automatic facial expression recognition (AFER) has been shown to work well when restricted to subjects with a limited range of ages, expressions, and intensities of expression. Recognition of the expressions of subjects across a large range of ages (including older people), expressions (compound emotions), and intensities (ranging from a neutral expression to the apex of the target expression) is harder and, to date, has not been studied in any particular depth. This thesis focuses on studying the influence of these problems on the accuracy of AFER. The main concern is to investigate the possibilities that can be used for modelling facial expression recognition against the impact of the problems under study in order to ensure the solution is more generalized and effective. Since the face image is a collection of texture and shape parameters, the study starts by using texture measurement methods to understand the influence of those problems on face texture features and hence on texture-based AFER. Our first contribution shows that by using binary robust independent elementary features (BRIEF) (Calonder et al., 2012), we can develop a new face descriptor model that is able to describe face images and can generalize to new data sets. The BRIEF descriptor is able to generate the discriminative features globally from the image with an explicit shape. However, when BRIEF is used to generate feature from an image with no explicit shape such as the face image, BRIEF is unable to generate discriminative feature. We thus propose to use BRIEF locally to ensure that each pixel in the image is evaluated locally to capture the local shape surrounding around it. Empirical and comprehensive evaluation using three facial expression datasets demonstrates that this model gives satisfactory performance compared to other local face descriptors techniques evaluated on the same datasets. The study also shows that the patterns of the problems under consideration have a significant effect on the face texture features and on the accuracy of texture-based AFER. The study is then extended by using shape measurement methods to investigate the influence of those problems on the face shape features and hence on shape-based AFER. Our second contribution shows that by using random forest regression voting in a constrained local model (RFRV-CLM) framework (Cootes et al., 2012; Lindner et al., 2015), we can develop a fully automated facial expression localization (FEL) system that is able to detect the facial key points in a multiple-stage (coarse-to-fine) scheme and can generalize accurately to new data sets with a wide range of variations of facial appearances. Empirical and comprehensive evaluation using five different facial expression datasets demonstrates that this model gives excellent agreement with ground truth data and outperforms the results of alternative methods evaluated on the same datasets. The study also shows that the patterns of the problems under study have a significant effect on the performance of FEL, and that the FEL based on RFRV-CLM achieved good performance against that effect. It also demonstrates that appearance-based AFER (combining shape with texture) gives better results than texture-based AFER. Our final contribution builds on the second and it is the development of an age-based AFER system that explicitly estimates age group and expression in a single framework. In this system, we show that by using the age information, in particular apparent age since some people might look younger or older than their real age, as prior knowledge to the expression recognition through using a weighted combination rule of a set of age group classifier and age-specific expression classifiers, we can significantly eliminate the influence of age features on the expression classification accuracy. Tested on three age-expression datasets, we show that the results of our novel system were encouraging in comparison to the state-of-art systems which ignore age and alternative models recently applied to the problem. In summary, the results of the BRIEF-based face descriptor, RFRV-CLM-based FEL, and age-based AFER are encouraging and could be basic building blocks for many face applications in computer vision such face detection, face recognition … etc.