AI and Deep Learning in Biometric Security: Trends, Potential, and Challenges [Paperback]

This book provides an in-depth overview of artificial intelligence and deep learning approaches with case studies to solve problems associated with biometric security such as authentication, indexing, template protection, spoofing attack detection, ROI detection, gender classification etc.

This text highlights a showcase of cutting-edge research on the use of convolution neural networks, autoencoders, recurrent convolutional neural networks in face, hand, iris, gait, fingerprint, vein, and medical biometric traits. It also provides a step-by-step guide to understanding deep learning concepts for biometrics authentication approaches and presents an analysis of biometric images under various environmental conditions.

This book is sure to catch the attention of scholars, researchers, practitioners, and technology aspirants who are willing to research in the field of AI and biometric security.

1. Deep Learning-Based Hyperspectral Multimodal Biometric Authentication System Using Palmprint and Dorsal Hand Vein. 2. Cancelable Biometrics for Template Protection: Future Directives with Deep Learning. 3. On Training Generative Adversarial Network for Enhancement  of Latent Fingerprints. 4. DeepFake Face Video Detection Using Hybrid Deep Residual Networks nad LSTM Architecture. 5. Multi-spectral Short-Wave Infrared Sensors and Convolutional Neural Networks for Biometric Presentation Attack Detection. 6. AI-Based Approach for Person Identification Using ECG Biometric. 7. Cancelable Biometric Systems from Research to Reality: The Road Less Travelled. 8. Gender Classification under Eyeglass Occluded Ocular Region: An Extensive Study Using Multi-spectral Imaging. 9. Investigation of the Fingernail Plate for Biometric Authentication using Deep Neural Networks. 10. Fraud Attack Detection in Remote Verification systems for Non-enrolled Users. 11. Indexing on Biometric Databases. 12. Iris Segmentation in the Wild Using El¤