314 research outputs found
Multimodal score-level fusion using hybrid ga-pso for multibiometric system
Due to the limitations that unimodal systems suffer from, Multibiometric systems have gained much interest in the research community on the grounds that they alleviate most of these limitations and are capable of producing better accuracies and performances. One of the important steps to reach this is the choice of the fusion techniques utilized. In this paper, a modeling step based on a hybrid algorithm, that includes Particle Swarm Optimization and Genetic Algorithm, is proposed to combine two biometric modalities at the score level. This optimization technique is employed to find the optimum weights associated to the modalities being fused. An analysis of the results is carried out on the basis of comparing the EER accuracies and ROC curves of the fusion techniques. Furthermore, the execution speed of the hybrid approach is discussed and compared to that of the single optimization algorithms, GA and PS
Generation of artificial facial drug abuse images using Deep De-identified anonymous Dataset augmentation through Genetics Algorithm (3DG-GA)
In biomedical research and artificial intelligence, access to large,
well-balanced, and representative datasets is crucial for developing
trustworthy applications that can be used in real-world scenarios. However,
obtaining such datasets can be challenging, as they are often restricted to
hospitals and specialized facilities. To address this issue, the study proposes
to generate highly realistic synthetic faces exhibiting drug abuse traits
through augmentation. The proposed method, called "3DG-GA", Deep De-identified
anonymous Dataset Generation, uses Genetics Algorithm as a strategy for
synthetic faces generation. The algorithm includes GAN artificial face
generation, forgery detection, and face recognition. Initially, a dataset of
120 images of actual facial drug abuse is used. By preserving, the drug traits,
the 3DG-GA provides a dataset containing 3000 synthetic facial drug abuse
images. The dataset will be open to the scientific community, which can
reproduce our results and benefit from the generated datasets while avoiding
legal or ethical restrictions
Analyse thermofiabiliste de matériaux poreux céramiques à haut taux de densification
National audienceSee http://hal.archives-ouvertes.fr/docs/00/59/27/90/ANNEX/r_IF8J8H1Q.pd
Generative Adversarial Networks for anonymous Acneic face dataset generation
It is well known that the performance of any classification model is
effective if the dataset used for the training process and the test process
satisfy some specific requirements. In other words, the more the dataset size
is large, balanced, and representative, the more one can trust the proposed
model's effectiveness and, consequently, the obtained results. Unfortunately,
large-size anonymous datasets are generally not publicly available in
biomedical applications, especially those dealing with pathological human face
images. This concern makes using deep-learning-based approaches challenging to
deploy and difficult to reproduce or verify some published results. In this
paper, we suggest an efficient method to generate a realistic anonymous
synthetic dataset of human faces with the attributes of acne disorders
corresponding to three levels of severity (i.e. Mild, Moderate and Severe).
Therefore, a specific hierarchy StyleGAN-based algorithm trained at distinct
levels is considered. To evaluate the performance of the proposed scheme, we
consider a CNN-based classification system, trained using the generated
synthetic acneic face images and tested using authentic face images.
Consequently, we show that an accuracy of 97,6\% is achieved using
InceptionResNetv2. As a result, this work allows the scientific community to
employ the generated synthetic dataset for any data processing application
without restrictions on legal or ethical concerns. Moreover, this approach can
also be extended to other applications requiring the generation of synthetic
medical images. We can make the code and the generated dataset accessible for
the scientific community
Compression of Biomedical Images and Signals
During the last decade, image and signal compression for storage and transmission purpose has seen a great expansion. But what about medical data compression? Should a medical image or a physiological signal be processed and compressed like any other data? The progress made in imaging systems, storing systems and telemedicine makes compression in this field particularly interesting. However, this compression has to be adapted to the specificities of biomedical data which contain diagnosis information.As such, this book offers an overview of compression techniques applied to medical data, including: physiological signals, MRI, X-ray, ultrasound images, static and dynamic volumetric images.Researchers, clinicians, engineers and professionals in this area, along with postgraduate students in the signal and image processing field, will find this book to be of great interest
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