AI in Fingerprint Recognition ‍

It has been known that some fingerprints are hard to scan or recognize due to skin conditions, damaged fingerprint, scars or small fingerprint surface area. Low quality, old, cropped, damaged images of fingerprints are some other challenges.

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Machine learning techniques such as Artificial Neural Networks (ANN), Deep Neural Networks (DNN), Support Vector Machine (SVM) and Genetic Algorithms (GA) play an important part for delivering non-common solutions for fingerprint identification problems.

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According to a research report, deep learning, especially Convolutional Neural Network (CNN), has made big success in computer vision and pattern recognition fields, as it doesn’t require special feature extraction. Deep learning automatically learns features and structures under a sufficient number of training data. These advantages of CNNs makes it perfect for various jobs in automatic fingerprint recognition/identification systems: including segmentation, classification, feature extraction (minutiae points and singular points), ridge orientation estimation etc.