Impact of Patch-Size on Classification Accuracy of Latent Fingerprint Image in Stacked Convolutional Auto-encoder based Segmentation and Detection

Latent fingerprints are (un)intentional finger skin impressions left as ridge patterns at crime scenes. The significant challenge in latent fingerprint segmentation is extracting complex, multiple, noisy foreground fingermarks while maintaining the performance of the system. The work presented in th...

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Vydané v:	2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) s. 367 - 372
Hlavní autori:	Chhabra, Megha, Shukla, Manoj Kumar, Ravulakollu, Kiran Kumar
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 07.10.2020
Predmet:	Autoencoder Convolutional neural network Classification Convolutional neural networks Fingerprint recognition Image matching Image processing Image segmentation Latent fingerprint Neural networks Noise measurement Segmentation Skin
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