An Investigation of Face and Fingerprint Feature-Fusion Guidelines

2017

SACJ

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The feature level, unlike the match score level, lacks multi-modal fusion guidelines. This work demonstrates a new approach for improved image-based biometric feature-fusion. The approach extracts and combines the face, fingerprint and palmprint at the feature level for improved human identification accuracy. Feature-fusion guidelines, proposed in our recent work, are extended by adding a new face segmentation method and the support vector machine classifier. The new face segmentation method improves the face identification equal error rate (EER) by 10%. The support vector machine classifier combined with the new feature selection approach, proposed in our recent work, outperforms other classifiers when using a single training sample. Feature-fusion guidelines take the form of strengths and weaknesses as observed in the applied feature processing modules during preliminary experiments. The guidelines are used to implement an effective biometric fusion system at the feature level, using a novel feature-fusion methodology, reducing the EER of two groups of three datasets namely: SDUMLA face, SDUMLA fingerprint and IITD palmprint; MUCT Face, MCYT Fingerprint and CASIA Palmprint.

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Section: Methodology For Applying Guidelinesmentioning

confidence: 98%

Feature-Fusion Guidelines for Image-Based Multi-Modal Biometric Fusion

2017

SACJ

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“…Therefore, a custom architecture is proposed for comparative purposes using VGG-16 as a basis and utilizing Keras-Tuner to iteratively determine the optimal number of blocks of layers by a hyperparameter search. 5 The high-level pruned structure is the first 2/5 blocks of the VGG-16, and one Fully Connected (FC) flattened layer and finally, a softmax classification layer.…”

Section: Cnn Architecture Considerationsmentioning

confidence: 99%

“…One training sample: the optimal resolution was 32 × 32 to 64 × 64 , except LBPH which preferred the segmented resolution (about 200 × 200 ). LBPH yield better DIR, but at poor FPIR values compared with the rest [taken from[5]]…”

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confidence: 99%

Deep Palmprint Recognition with Alignment and Augmentation of Limited Training Samples

2021

SN COMPUT. SCI.

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This paper builds upon a previously proposed automatic palmprint alignment and classification system. The proposed system was geared towards palmprints acquired from either contact or contactless sensors. It was robust to finger location and fist shape changes—accurately extracting the palmprints in images without fingers. An extension to this previous work includes comparisons of traditional and deep learning models, both with hyperparameter tuning. The proposed methods are compared with related verification systems and a detailed evaluation of open-set identification. The best results were yielded by a proposed Convolutional Neural Network, based on VGG-16, and outperforming tuned VGG-16 and Xception architectures. All deep learning algorithms are provided with augmented data, included in the tuning process, enabling significant accuracy gains. Highlights include near-zero and zero EER on IITD-Palmprint verification using one training sample and leave-one-out strategy, respectively. Therefore, the proposed palmprint system is practical as it is effective on data containing many and few training examples.

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Improved Automatic Face Segmentation and Recognition for Applications with Limited Training Data

Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation

2017