Biometric Identification

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Hybrid Feature Learning for Handwriting Verification
Nov 19, 2018Seyed Mohammad Abuzar Hashemi, Mihir Chauhan, Jun Chu +1Biometric Identification Representation Learning
The Department of Computer Science and Engineering The State University of New York at Buffalo Buffalo, NY, USA
Leveraging Avatar Fingerprinting: A Multi-Generator Photorealistic Talking-Head Public Database and Benchmark
Date pendingLaura Pedrouzo-Rodriguez, Luis F. Gomez, Ruben Tolosana +4Biometric Identification
BiometricsAI, Universidad Autónoma de Madrid, Madrid, 28049, Spain · Department of Mathematics, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, 35001, Spain

Nov 19, 2018, cs.CVJ/K move · Enter open · S save

Hybrid Feature Learning for Handwriting Verification

Seyed Mohammad Abuzar Hashemi, Mihir Chauhan, Jun Chu, Sargur Srihari

The Department of Computer Science and Engineering The State University of New York at Buffalo Buffalo, NY, USA

Biometric Identification Representation Learning

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We propose an effective Hybrid Deep Learning (HDL) architecture for the task of determining the probability that a questioned handwritten word has been written by a known writer. HDL is an amalgamation of Auto-Learned Features (ALF) and Human-Engineered Features (HEF). To extract auto-learned features we use two methods: First, Two Channel Convolutional Neural Network (TC-CNN); Second, Two Channel Autoencoder (TC-AE). Furthermore, human-engineered features are extracted by using two methods: First, Gradient Structural Concavity (GSC); Second, Scale Invariant Feature Transform (SIFT). Experiments are performed by complementing one of the HEF methods with one ALF method on 150000 pairs of samples of the word "AND" cropped from handwritten notes written by 1500 writers. Our results indicate that HDL architecture with AE-GSC achieves 99.7% accuracy on seen writer dataset and 92.16% accuracy on shuffled writer dataset which out performs CEDAR-FOX, as for unseen writer dataset, AE-SIFT performs comparable to this sophisticated handwriting comparison tool.

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Latest papers 92

Hybrid Feature Learning for Handwriting Verification

Leveraging Avatar Fingerprinting: A Multi-Generator Photorealistic Talking-Head Public Database and Benchmark