WebJun 15, 2024 · PDF On Jun 15, 2024, Patrick Cerna and others published Machine Learning Biometric Attendance System using Fingerprint Fuzzy Vault Scheme Algorithm and Multi-Task Convolution Neural Network Face ... WebApr 13, 2024 · Inspired by this, this paper proposes a multi-agent deep reinforcement learning with actor-attention-critic network for traffic light control (MAAC-TLC) algorithm. In MAAC-TLC, each agent introduces the attention mechanism in the process of learning, so that it will not pay attention to all the information of other agents indiscriminately, but ...
Using Deep Learning for finger-vein based biometric …
WebFeb 4, 2024 · Deep convolutional neural networks have achieved remarkable improvements in facial recognition performance. Similar kinds of developments, e.g. deconvolutional neural networks, have shown impressive results for reconstructing face images from their corresponding embeddings in the latent space.This poses a severe security risk which … WebNov 30, 2024 · Deep learning-based models have been very successful in achieving state-of-the-art results in many of the computer vision, speech recognition, and natural language processing tasks in the last few years. These models seem a natural fit for handling the ever-increasing scale of biometric recognition problems, from cellphone authentication … siedle intercom uk
Deep face fuzzy vault: Implementation and performance
WebFeb 6, 2024 · We present a framework for the deep learning-based HAR system. We employ a deep Convolutional Network in conjunction with Deep Recurrent LSTM networks for prediction and recognition of activities. For the first time, we innovated a Fuzzy Soft-max classifier that classify the output of LSTM Blocks to each of the activity classes. WebPreviously, I collaborated on a computer science project, enhancing security of the biometric data stored in a system by using deep learning … WebThis paper proposed a framework for human gait recognition based on deep learning and Bayesian optimization. The proposed framework includes both sequential and parallel steps. In the first step, optical flow-based motion regions are extracted and utilized to train the fine-tuned EfficentNet-B0 deep model. siedler 3 map editor download