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Full-Text Articles in Computer Engineering
Learning Domain Invariant Information To Enhance Presentation Attack Detection In Visible Face Recognition Systems, Jennifer Hamblin
Learning Domain Invariant Information To Enhance Presentation Attack Detection In Visible Face Recognition Systems, Jennifer Hamblin
Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research
Face signatures, including size, shape, texture, skin tone, eye color, appearance, and scars/marks, are widely used as discriminative, biometric information for access control. Despite recent advancements in facial recognition systems, presentation attacks on facial recognition systems have become increasingly sophisticated. The ability to detect presentation attacks or spoofing attempts is a pressing concern for the integrity, security, and trust of facial recognition systems. Multi-spectral imaging has been previously introduced as a way to improve presentation attack detection by utilizing sensors that are sensitive to different regions of the electromagnetic spectrum (e.g., visible, near infrared, long-wave infrared). Although multi-spectral presentation attack …
An End-To-End Trainable Method For Generating And Detecting Fiducial Markers, J Brennan Peace
An End-To-End Trainable Method For Generating And Detecting Fiducial Markers, J Brennan Peace
Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research
Existing fiducial markers are designed for efficient detection and decoding. The methods are computationally efficient and capable of demonstrating impressive results, however, the markers are not explicitly designed to stand out in natural environments and their robustness is difficult to infer from relatively limited analysis. Worsening performance in challenging image capture scenarios - such as poorly exposed images, motion blur, and off-axis viewing - sheds light on their limitations. The method introduced in this work is an end-to-end trainable method for designing fiducial markers and a complimentary detector. By introducing back-propagatable marker augmentation and superimposition into training, the method learns …