Rediscovering Minutiae Matching through One Shot Learning’s Siamese Framework in Poor Quality Footprint Images

Footprint biometrics is one of the emerging techniques, which can be utilized in different security systems. A human footprint has unique traits which is sufficient to recognize any person. Existing work evaluates the shape features and texture features but very few authors have explored minutiae features, hence this article provides a study based on minutiae features. The current State-of-the-art methods utilize machine learning techniques, which suffer from low accuracy in case of poor-quality of data. These machine learning techniques provide approx 97% accuracy while using good quality images but are not able to perform well when we use poor quality images. We have proposed a minutiae matching system based on deep learning techniques which is able to handle samples with adequate noise. We have used Convolution Neural Network for the feature extraction. It uses two different ridge flow estimation methods i.e. ConvNet-based and dictionary-based. Furthermore, fingerprint-matching metrics are used for footprint feature evaluation. We initially employed a contrastive-based loss function, resulting in an accuracy of 56%. Subsequently, we adapted our approach by implementing a distance-based loss function, which improved the accuracy to 66%.