Titlebook: Deep Learning for Biometrics; Bir Bhanu,Ajay Kumar Book 2017 Springer International Publishing AG, part of Springer Nature 2017 Deep Learn

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Latent Fingerprint Image Segmentation Using Deep Neural Networkd on RBMs learns fingerprint image patches in two phases. The first phase (unsupervised pre-training) involves learning an identity mapping of the input image patches. In the second phase, fine-tuning and gradient updates are performed to minimize the cost function on the training dataset. The resul

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Iris Segmentation Using Fully Convolutional Encoder–Decoder Networksd networks, we apply a selection of conventional (non-CNN) iris segmentation algorithms on the same datasets, and similarly evaluate their performances. The results then get compared against those obtained from the FCEDNs. Based on the results, the proposed networks achieve superior performance over

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Deep Triplet Embedding Representations for Liveness Detectionfingerprints are dissimilar from the ones generated artificially. A variant of the triplet objective function is employed, that considers patches taken from two real fingerprint and a replica (or two replicas and a real fingerprint), and gives a high penalty if the distance between the matching coup