A Binarization Scheme for Face Recognition Based on Multi-Scale Block Local Binary Patterns

Schlett, Torsten; Rathgeb, Christian; Busch, Christoph

doi:10.1109/biosig.2016.7736901

Cited by 7 publications

(5 citation statements)

References 11 publications

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“…In Lim et al [16], the authors propose two new encoding schemes (LSSC and PLSSC -(Partially) Linearly Separable Subcode) which exhibit full-ideal and near-ideal separability capabilities, respectively. Schlett et al [17] describe a simple, yet effective, scheme for binarising multiscale LBP histograms.…”

Section: Related Workmentioning

confidence: 99%

Benchmarking Binarisation Schemes for Deep Face Templates

Drozdowski

Struck

Rathgeb

et al. 2018

2018 25th IEEE International Conference on Image Processing (ICIP)

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Feature vectors extracted from biometric characteristics are often represented using floating point values. It is, however, more appealing to store and compare feature vectors in a binary representation, since it generally requires less storage and facilitates efficient comparators which utilise intrinsic bit operations. Furthermore, the binary representations are very often necessary for some specific application scenarios, e.g. template protection and indexing. In recent years, usage of deep neural networks for facial recognition has vastly improved the biometric performance of said systems. In this paper, various binarisation schemes are applied to such feature vectors and benchmarked for biometric performance. It is shown that with only a negligible drop in biometric performance, the storage space and computational requirements can be vastly decreased.

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Section: Related Workmentioning

confidence: 99%

Benchmarking Binarisation Schemes for Deep Face Templates

Drozdowski

Struck

Rathgeb

et al. 2018

2018 25th IEEE International Conference on Image Processing (ICIP)

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“…[9], Drozdowski et al benchmark dataindependent binarisation methods such as Refs. [10][11][12][13][14][15][16][17]. These rule-based methods directly quantise the projected values with a threshold or use an orthogonal matrix to obtain the binary codes.…”

Section: Related Workmentioning

confidence: 99%

“… Note : The baseline system is the system used in [17]. The results in the second row (row ‘128*’) are obtained by applying a median binarisation on the output of the CNN used in Ref.…”

Section: Biometric Performance Of the Binary Representationsmentioning

confidence: 99%

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Locality preserving binary face representations using auto‐encoders

2022

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Crypto‐biometric schemes, such as fuzzy commitment, require binary sources. A novel approach to binarising biometric data using Deep Neural Networks applied to facial biometric data is introduced. The binary representations are evaluated on the MOBIO and the Labelled Faces in the Wild databases, where their biometric recognition performance and entropy are measured. The proposed binary embeddings give a state‐of‐the‐art performance on both databases with almost negligible degradation compared to the baseline. The representations' length can be controlled. Using a pretrained convolutional neural network and training the model on a cleaned version of the MS‐celeb‐1M database, binary representations of length 4096 bits and 3300 bits of entropy are obtained. The extracted representations have high entropy and are long enough to be used in crypto‐biometric systems, such as fuzzy commitment. Furthermore, the proposed approach is data‐driven and constitutes a locality preserving hashing that can be leveraged for data clustering and similarity searches. As a use case of the binary representations, a cancellable system is created based on the binary embeddings using a shuffling transformation with a randomisation key as a second factor.

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“…Such comparators can take advantage of the more efficient bitwise operators, thereby reducing the computational workload. An illustrative example can be seen in [144] (and a simpler one in [143]), where various bit allocation schemes for float-based feature vectors generated by neural network-based systems are benchmarked. In [142], a new representation is extracted from minutiae points, which can be further binarised to accelerate the biometric template comparisons.…”

Section: Binarisationmentioning

confidence: 99%

Computational workload in biometric identification systems: an overview

2019

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Computational workload is one of the key challenges in biometric identification systems. The naïve retrieval method based on an exhaustive search becomes impractical with the growth of the number of the enrolled data subjects. Consequently, in recent years, many methods with the aim of reducing or optimising the computational workload, and thereby speeding-up the identification transactions, in biometric identification systems have been developed. In this article, a taxonomy for conceptual categorisation of such methods is presented, followed by a comprehensive survey of the relevant academic publications, including computational workload reduction and software/hardware-based acceleration. Lastly, the pertinent technical considerations and trade-offs of the surveyed methods are discussed, along with an industry perspective, and open issues/challenges in the field.

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A Binarization Scheme for Face Recognition Based on Multi-Scale Block Local Binary Patterns

Cited by 7 publications

References 11 publications

Benchmarking Binarisation Schemes for Deep Face Templates

Benchmarking Binarisation Schemes for Deep Face Templates

Locality preserving binary face representations using auto‐encoders

Computational workload in biometric identification systems: an overview

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