Core Concepts
Integrating neural networks with biometric features enhances accuracy and overall security compared to other techniques.
Abstract
The paper explores the use of neural networks in various biometric authentication methods, including face recognition, fingerprint recognition, finger-vein identification, iris recognition, and gait recognition. The key insights are:
Face Recognition:
Neural networks can effectively detect and classify facial features, outperforming traditional methods in accuracy and computational efficiency.
Neural networks are resilient to presentation attacks, such as using printed photographs or silicone masks, which can trick conventional face recognition systems.
Fingerprint Recognition:
Neural networks can accurately match fingerprint minutiae (ridge endings and bifurcations) for identification, achieving high accuracy rates.
The neural network approach requires less image preprocessing compared to traditional fingerprint recognition methods.
Finger-vein Identification:
Convolutional neural networks (CNNs) can effectively extract and classify finger-vein patterns, eliminating the need for complex image processing and segmentation.
The proposed CNN-based approach achieves 100% identification accuracy on a 50-subject dataset and 99.38% accuracy on an 81-subject dataset.
Iris Recognition:
Integrating neural networks with genetic algorithms can optimize the iris recognition process, leading to higher accuracy and reduced training time compared to conventional neural network methods.
Gait Recognition:
Deep learning neural networks, trained on novel geometric features (joint relative cosine dissimilarity and joint relative triangle area), outperform other prominent Kinect-based gait recognition techniques.
The proposed deep learning approach achieves superior accuracy, precision, recall, and F-score compared to existing methods.
The paper concludes that the utilization of neural networks, along with biometric features, not only enhances accuracy but also contributes to overall better security compared to other biometric authentication techniques.
Stats
"Biometric traits encompassing physical and behavioral characteristics such as fingerprints, face, iris, gait, and voice are inherent and unique to each individual."
"The neural network (N.N) classifier yields the highest accuracy when combined with homomorphic filtering and histogram equalization, mainly when using the PCA feature extractor."
"The proposed CNN-based approach achieves 100% identification accuracy on a 50-subject dataset and 99.38% accuracy on an 81-subject dataset."
"The accuracy, precision, recall, and F-score of the proposed neural network architecture have been confirmed through experimental validation."
Quotes
"Neural networks can derive meaning from complicated data."
"A neural network is characterized by adaptive learning capabilities, allowing it to learn how to perform tasks based on the data provided during training."
"Neural Networks prediction accuracy is generally high."