Facial emotion recognition finds many real applications in the daily life like human robot interaction, eLearning, healthcare, customer services etc. The task of facial emotion recognition is not easy due to the difficulty in determining the effective feature set that can recognize the emotion conveyed within the facial expression accurately. Graph mining techniques are exploited in this paper to solve facial emotion recognition problem. After determining positions of facial landmarks in face region, twelve different graphs are constructed using four facial components to serve as a source for sub-graphs mining stage using gSpan algorithm. In each group, the discriminative set of sub-graphs are selected and fed to Deep Belief Network (DBN) for classification purpose. The results obtained from the different groups are then fused using Naïve Bayes classifier to make the final decision regards the emotion class. Different tests were performed using Surrey Audio-Visual Expressed Emotion (SAVEE) database and the achieved results showed that the system gives the desired accuracy (100%) when fusion decisions of the facial groups. The achieved result outperforms state-of-the-art results on the same database.
Face detection systems are based on the assumption that each individual has a unique face structure and that computerized face matching is possible using facial symmetry. Face recognition technology has been employed for security purposes in many organizations and businesses throughout the world. This research examines the classifications in machine learning approaches using feature extraction for the facial image detection system. Due to its high level of accuracy and speed, the Viola-Jones method is utilized for facial detection using the MUCT database. The LDA feature extraction method is applied as an input to three algorithms of machine learning approaches, which are the J48, OneR, and JRip classifiers. The experiment’s
... Show MoreAim: The purpose of this study was to analyze the patterns of facial fractures in children and to compare them between preschool- and school-aged children. Materials and methods: This retrospective observational study included 57 children with facial fractures. The variables analyzed were the age of the patients—divided into a preschool-aged group (0–5 years) and a school-aged group (6–12 years)—gender, cause of trauma, the facial bones involved, the pattern of fracture, the modality of treatment used, the time between injury and treatment, and the postoperative complications. Results: The incidence of facial fractures in children ≤12 years was 30.2%. The patients consisted of 40 (70.2%) males and 17 (29.8%) females, and most pati
... Show MoreHM Al-Dabbas, RA Azeez, AE Ali, Iraqi Journal of Science, 2023
In this paper, Nordhaus-Gaddum type relations on open support independence number of some derived graphs of path related graphs under addition and multiplication are studied.
This research deals with a shrinking method concernes with the principal components similar to that one which used in the multiple regression “Least Absolute Shrinkage and Selection: LASS”. The goal here is to make an uncorrelated linear combinations from only a subset of explanatory variables that may have a multicollinearity problem instead taking the whole number say, (K) of them. This shrinkage will force some coefficients to equal zero, after making some restriction on them by some "tuning parameter" say, (t) which balances the bias and variance amount from side, and doesn't exceed the acceptable percent explained variance of these components. This had been shown by MSE criterion in the regression case and the percent explained v
... Show MoreThis study was designed to evaluate the role of single session autologous facial fat grafting in correcting facial asymmetries after mixing it with platelet-rich fibrin (PRF) and injecting them into rich vascular facial muscular plane.
Fifteen patients (12 females and 3 males) with age ranging from 18 years to 40 years were included in this study and followed up during 6 months, all the patients were treated in the Al-Shaheed Ghazi Al-Hariri for specialized surgeries hospital (Medical City, Baghdad, Iraq).
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Vascular patterns were seen to be a probable identification characteristic of the biometric system. Since then, many studies have investigated and proposed different techniques which exploited this feature and used it for the identification and verification purposes. The conventional biometric features like the iris, fingerprints and face recognition have been thoroughly investigated, however, during the past few years, finger vein patterns have been recognized as a reliable biometric feature. This study discusses the application of the vein biometric system. Though the vein pattern can be a very appealing topic of research, there are many challenges in this field and some improvements need to be carried out. Here, the researchers reviewed
... Show MoreThis paper proves the existence of face antimagic labeling for double duplication of barycentric subdivision of cycle and some other graphs
Mining association rules is a popular and well-studied method of data mining tasks whose primary aim is the discovers of the correlation among sets of items in the transactional databases. However, generating high- quality association rules in a reasonable time from a given database has been considered as an important and challenging problem, especially with the fast increasing in database's size. Many algorithms for association rules mining have been already proposed with promosing results. In this paper, a new association rules mining algorithm based on Bees Swarm Optimization metaheuristic named Modified Bees Swarm Optimization for Association Rules Mining (MBSO-ARM) algorithm is proposed. Results show that the proposed algorithm can
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