Enhancing child safety with accurate fingerprint identification using deep learning technology

Utilizing deep learning algorithms to differentiate the fingerprints of children can greatly enhance their safety. This advanced technology enables precise identification of individual children, facilitating improved monitoring and tracking of their activities and movements. This can effectively prevent abductions and other forms of harm, while also providing a valuable resource for law enforcement and other organizations responsible for safeguarding children. Furthermore, the use of deep learning algorithms minimizes the potential for errors and enhances the overall accuracy of fingerprint recognition. Overall, implementing this technology has immense potential to significantly improve the safety of children in various settings. Our experiments have demonstrated that deep learning significantly enhances the accuracy of fingerprint recognition for children. The model accurately classified fingerprints with an overall accuracy rate of 93%, surpassing traditional fingerprint recognition techniques by a significant margin. Additionally, it correctly identified individual children's fingerprints with an accuracy rate of 89%, showcasing its ability to distinguish between different sets of fingerprints belonging to different children

Face Image and Video Analysis in Biometrics and Health Applications

Computer Vision (CV) enables computers and systems to derive meaningful information from acquired visual inputs, such as images and videos, and make decisions based on the extracted information. Its goal is to acquire, process, analyze, and understand the information by developing a theoretical and algorithmic model. Biometrics are distinctive and measurable human characteristics used to label or describe individuals by combining computer vision with knowledge of human physiology (e.g., face, iris, fingerprint) and behavior (e.g., gait, gaze, voice). Face is one of the most informative biometric traits. Many studies have investigated the human face from the perspectives of various different disciplines, ranging from computer vision, deep learning, to neuroscience and biometrics. In this work, we analyze the face characteristics from digital images and videos in the areas of morphing attack and defense, and autism diagnosis. For face morphing attacks generation, we proposed a transformer based generative adversarial network to generate more visually realistic morphing attacks by combining different losses, such as face matching distance, facial landmark based loss, perceptual loss and pixel-wise mean square error. In face morphing attack detection study, we designed a fusion-based few-shot learning (FSL) method to learn discriminative features from face images for few-shot morphing attack detection (FS-MAD), and extend the current binary detection into multiclass classification, namely, few-shot morphing attack fingerprinting (FS-MAF). In the autism diagnosis study, we developed a discriminative few shot learning method to analyze hour-long video data and explored the fusion of facial dynamics for facial trait classification of autism spectrum disorder (ASD) in three severity levels. The results show outstanding performance of the proposed fusion-based few-shot framework on the dataset. Besides, we further explored the possibility of performing face micro- expression spotting and feature analysis on autism video data to classify ASD and control groups. The results indicate the effectiveness of subtle facial expression changes on autism diagnosis

Individual Differences in Speech Production and Perception

Inter-individual variation in speech is a topic of increasing interest both in human sciences and speech technology. It can yield important insights into biological, cognitive, communicative, and social aspects of language. Written by specialists in psycholinguistics, phonetics, speech development, speech perception and speech technology, this volume presents experimental and modeling studies that provide the reader with a deep understanding of interspeaker variability and its role in speech processing, speech development, and interspeaker interactions. It discusses how theoretical models take into account individual behavior, explains why interspeaker variability enriches speech communication, and summarizes the limitations of the use of speaker information in forensics

Brain Structural Maturation and Cognitive Abilities in Early Life

The first two years of life mark the most dynamic period of postnatal brain maturation, during which time cortical expansion and myelination reach peak developmental rates. Cortical morphology and white matter (WM) microstructure have been linked to cognition in older adults and children, yet we know remarkably little about how the brain matures to support emergent cognition. This is a critical gap in knowledge, as the first years of life mark a sensitive period in child development when atypical brain and behavioral phenotypes may become apparent. In this report, we examined cortical thickness (CT), surface area (SA), and WM fiber integrity in 450 typically-developing children at birth, age 1, and age 2 in association with assessments of motor, language, and general cognitive abilities at ages 1 and 2. Results revealed that generally thicker, larger cortices and more mature WM tract properties in early life related to better performance on cognitive tasks, suggesting that increased synaptogenesis, elaborations in dendritic arborization, and myelination may confer benefits for infant cognitive development. We found several expected brain-cognition relationships, with CT in regions associated with motor planning and execution and regions associated with language processing and production related to motor and language scores, respectively. Results between cognition and WM integrity were less specific, with tract properties across many fibers spanning the brain relating to cognition across domains. This finding, along with the fact that the majority of significant WM results were of a predictive nature, prompted further study into the organization of WM at birth and future outcomes. Using a deep learning approach, we successfully predicted 2-year cognitive outcomes using WM connectivity patterns at birth. Taken together, these results suggest that cortical structure and the organization and microstructural integrity of WM pathways at birth serve as a foundation upon which subsequent fine-tuning of brain structure takes place to support emergent cognition in infancy and toddlerhood. These findings offer novel insight into how prenatal and postnatal brain structural maturation support infant and toddler cognitive abilities and fills important gaps in our current understanding of the neurobiology of emergent language, motor, and cognitive abilities in early life.Doctor of Philosoph

Emotional expressions reconsidered: challenges to inferring emotion from human facial movements

It is commonly assumed that a person’s emotional state can be readily inferred from his or her facial movements, typically called emotional expressions or facial expressions. This assumption influences legal judgments, policy decisions, national security protocols, and educational practices; guides the diagnosis and treatment of psychiatric illness, as well as the development of commercial applications; and pervades everyday social interactions as well as research in other scientific fields such as artificial intelligence, neuroscience, and computer vision. In this article, we survey examples of this widespread assumption, which we refer to as the common view, and we then examine the scientific evidence that tests this view, focusing on the six most popular emotion categories used by consumers of emotion research: anger, disgust, fear, happiness, sadness, and surprise. The available scientific evidence suggests that people do sometimes smile when happy, frown when sad, scowl when angry, and so on, as proposed by the common view, more than what would be expected by chance. Yet how people communicate anger, disgust, fear, happiness, sadness, and surprise varies substantially across cultures, situations, and even across people within a single situation. Furthermore, similar configurations of facial movements variably express instances of more than one emotion category. In fact, a given configuration of facial movements, such as a scowl, often communicates something other than an emotional state. Scientists agree that facial movements convey a range of information and are important for social communication, emotional or otherwise. But our review suggests an urgent need for research that examines how people actually move their faces to express emotions and other social information in the variety of contexts that make up everyday life, as well as careful study of the mechanisms by which people perceive instances of emotion in one another. We make specific research recommendations that will yield a more valid picture of how people move their faces to express emotions and how they infer emotional meaning from facial movements in situations of everyday life. This research is crucial to provide consumers of emotion research with the translational information they require

Host-pathogen interaction during Streptococcus pneumoniae colonization and infection

__Abstract__ Streptococcus pneumoniae was discovered by Sternberg and Pasteur in 1880. It took another six years to discover that this microorganism, called the pneumococcus, was the actual cause of bacterial pneumonia . Subsequently, this bacterium has been shown to provoke an impressive number of diseases, which can be roughly classified in respiratory and invasive. Respiratory diseases by S. pneumoniae are sinusitis, otitis media, bronchitis and pneumonia, of which the latter one may be complicated by septicemia . Disseminated invasive infections caused by the pneumococcus include sepsis, meningitis, endocarditis and arthritis. Morbidity and mortality are high both in the developing and the developed world: annually 3 million people die worldwide of pneumococcal infections. Approximately 1 million are children under the age of five years . Risk groups for pneumococcal disease are children younger than 2 years, elderly people and immunocompromised patients . In children, the increased risk for pneumococcal infections is mainly due to a relatively immature immune response to type II T-cell independent (TI-2) antigens such as capsular polysaccharides . In the elderly, the ability of both the innate as well as the adaptive immunity to respond to pneumococcal infection are thought to decline . In addition, higher rates of pneumococcal invasive diseases have been observed among populations such as Alaskan Natives, American Indians and African Americans . Patient groups who are at risk for particular variants of pneumococcal diseases are well defined. For example functional asplenia as in sickle cell disease as well as anatomic asplenia are serious risk factors for pneumococcal sepsis. This is due to the absence or dysfunction of the spleen, which is involved in systemic clearance of S. pneumoniae. In addition, complement deficiency and lower levels of circulating antibodies are thought to contribute to the increased susceptibility to pneumococcal infections in patients with sickle cell disease and other haemoglobinopathies . A relatively new risk group for pneumococcal meningitis are children with a cochlear implantation . Disease in these patients is thought to occur by the presence of a continuum between the outer ear and the inner skull. In general, patients with cerebrospinal fluid leakage, immunodeficiencies, chronic cardiovascular and pulmonary disease, HIV infections and diabetes mellitus are considered at risk for pneumococcal invasive disease

Outliers in biometrics : an a-contrario approach

This thesis addresses the problems of biometrics : how a persons identity could be determined or validated by using some physical or behavioral characteristic. Biometry is one of the main research topics in the field of pattern recognition due to its impact on several applications in security and human-machine interaction environments. Several works focus on the improvement of the features extracted in the particular system being presented (face, fingerprint or speech recognition among others), or the metrics used to compare such features, in this work the classification stage is particularly tackled.A statistical approach is presented based on a well-known a-contrario validation strategy. Techniques based on such framework have been widely used in the fields of image processing and computer vision for the detection and matching of visual features. In this work, the method ability to detect outliers/inliers is exploited to detect when two compared biometric samples correspond to the same person. This method is adapted and applied to each of the usual biometric tasks.First, it is applied to the task of biometric verification, modeling it as a two- class classification problem. The introduced strategy was validated using different datasets and compared against other state-of-the-art commonly used classification methods. Findings of this work have been presented at the 2014 International Conference on Pattern Recognition Applications and Methods (ICPRAM-2014), by applying the framework to the face recognition problem in particular. An extension of the conference article has been published as a journal article. In this thesis, the presented strategy is reviewed with an experimental evaluation done in several bigger datasets.Secondly, the a-contrario framework is applied to the identification task. The method is used to validate the confidence of an identification system outputs. What is normally called in the literature as System Response Reliability (SRR). Such problem has been thoroughly studied lately, the key advantages of using such control are analyzed and discussed. The obtained performance is validated on multiple datasets by comparing with other state-of-the-art approaches. This work has been presented on the 2016 International Conference of the Biometrics Special Interest Group (BIOSIG-2016).Finally, the framework is applied to biometric fusion. The key differences in such scenario and the corresponding proposed framework adaptations are analyzed. The proposed technique is evaluated in both artificially generated as real-scenario datasets. The performance is compared against other state-of-the-art statistically fusion strategie

Dietary Phenylalanine Requirement of Fingerling Oreochromis Niloticus (Linnaeus)

This study was conducted to determine the dietary phenylalanine for fingerling Oreochromis niloticus by conducting an 8 weeks experiment in a flow-through system (1-1.5L/min) at 28°C water temperature. Phenylalanine requirement was determined by feeding six casein-gelatin based amino acid test diets (350 g kg– 1 CP; 16.72 kJ g–1 GE) with graded levels of phenylalanine (4, 6.5, 9, 11.5, 14 and 16.5 g kg–1 dry diet) at a constant level (10 g kg–1) of dietary tyrosine to triplicate groups of fish (1.65±0.09 g) near to satiation. Absolute weight gain (AWG g fish-1), feed conversion ratio (FCR), protein deposition (PD%), phenylalanine retention efficiency (PRE%) and RNA/DNA ratio was found to improve with the increasing concentrations of phenylalanine and peaked at 11.5 g kg–1 of dry diet. Quadratic regression analysis of AWG, PD and PRE against varying levels of dietary phenylalanine indicated the requirement at 12.1, 11.6, and 12.7 g kg–1 dry diet, respectively and the inclusion of phenylalanine at 12.1 g kg–1 of dry diet, corresponding to 34.6 g kg–1 dietary protein is optimum for this fish. Based on above data, total aromatic amino acid requirement of fingerling O. niloticus was found to be 20.6 g kg–1 (12.1 g kg–1 phenylalanine+8.5 g kg–1 tyrosine) of dry diet, corresponding to 58.8 g kg–1 of dietary protein

2012-13 college catalog & student handbook

Central Carolina Technical College annually publishes a catalog with information about the university, student life, academic programs, and faculty and staff listings