TL;DR
The novelty of this work lies in the integration of a lightweight CNN-LSTM framework with LDA to achieve a balance between high recognition accuracy and computational efficiency, making it suitable for real-time facial authentication in edge and IoT environments.
The novelty of this work lies in the integration of a lightweight CNN-LSTM framework with LDA to achieve a balance between high recognition accuracy and computational efficiency, making it suitable for real-time facial authentication in edge and IoT environments.
A comparative analysis of existing studies is presented to highlight the evolution of deep learning techniques and their effectiveness in improving recognition accuracy and computational efficiency and emerging research directions are outlined to provide insights for future research.
A Self supervised Capsule Network (SS-CapsNet) integrates convolutional feature extraction, capsule based learning, dynamic routing and contrastive self supervised learning into a combined framework for robust face recognition.
Results show that the privacy preserving method introduced in this paper can effectively remove identity-related visual signals while at the same time keeping the level of face detection performance high, a feature which would make it most of all appealing for privacy critical computer vision applications.
The ablation results indicated that learned fusion, metric-learning losses, and liveness detection each contributed to performance, which support the feasibility of the proposed framework under the reported experimental conditions.
This work proposes a multimodal biometric framework that combines fingerprint and finger vein recognition for identity authentication that requires 156.2 ms per authentication request and about 24 % of CPU on average on a machine with no GPU, which confirms that it can operate in environments with limited computational...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
