IET Biometrics最新文献

Gait recognition uses video of human gait processed by computer vision methods to identify people based on walking style. The complexity introduced by covariates makes the previous methods less efficient and inaccurate. This study proposes an approach based on pose features to attempt gait recognition of people with an overcoat, carrying objects, or other covariates. It aims to estimate human locomotion using Convolutional Neural Networks. Gathering video data, extracting video frames in a particular order, posture estimation for each frame, using multilayer RNN for gait recognition from the pose, and obtaining one-dimensional object vectors, are all critical steps. Furthermore, these one-dimensional identification vectors are stored in a data set along with the name of the person walking in the video. The proposed data set is used to train a classification model to predict the person in a new video by first processing it to get its identification vector and then to use it as a test case in the classification model. A graphical user interface was also developed so that anyone with no programming or technical experience can easily use the tool. The developed application does everything for gait detection from mp4 videos by obtaining the identification vectors and saving them into the data set. Using this application, one can quickly identify the person walking in a video. The results obtained offered an accuracy from 60.88% to 95.23%.

This special issue of IET Biometrics, “BIOSIG 2021 Special Issue on Efficient, Reliable, and Privacy-Friendly Biometrics”, has as starting point the 2021 edition of the Biometric Special Interest Group (BIOSIG) conference. This special issue gathers works focussing on topics of biometric recognition put under the new light of fostering the efficiency, reliability and privacy of biometrics systems and methods.

The “BIOSIG 2021 Special Issue on Efficient, Reliable, and Privacy-Friendly Biometrics” issue contains 12 papers, several of them being extended versions of papers presented at the BIOSIG 2021 conference, dealing with concrete research areas within biometrics such as Presentation Attack Detection for Face and Iris, Biometric Template Protection Schemes and Deep Learning techniques for Biometrics.

Paper “Face Morphing Attacks and Face Image Quality: The Effect of Morphing and the Attack Detectability by Quality” was authored by Biying Fu and Naser Damer. This paper addresses the effect of morphing processes both on the perceptual image quality and the image utility in face recognition (FR) when compared to bona fide samples. This work provides an extensive analysis of the effect of morphing on face image quality, including both general image quality measures and face image utility measures, analysing six different morphing techniques and five different data sources using 10 different quality measures. The consistent separability between the quality scores of morphing attack and bona fide samples measured by certain quality measures sustains the proposal of performing unsupervised morphing attack detection (MAD) based on quality scores. The study looks into intra- and inter-dataset detectability to evaluate the generalisability of such a detection concept on different morphing techniques and bona fide sources. The results obtained point out that a set of quality measures, such as MagFace and CNNNIQA, can be used to perform unsupervised and generalised MAD with a correct classification accuracy of over 70%.

Paper “Pixel-Wise Supervision for Presentation Attack Detection on ID Cards” was authored by Raghavendra Mudgalgundurao, Patrick Schuch, Kiran Raja, Raghavendra Ramachandra, and Naser Damer. This paper addresses the problem of detection of fake ID cards that are printed and then digitally presented for biometric authentication purposes in unsupervised settings. The authors propose a method based on pixel-wise supervision, using DenseNet, to leverage minute cues on various artefacts such as moiré patterns and artefacts left by the printers. To test the proposed system, a new database was obtained from an operational system, consisting of 886 users with 433 bona fide, 67 print and 366 display attacks (not publicly available due to GPDR regulations). The proposed approach achieves better performance compared to handcrafted features and deep learning models, with an Equal Error Rate (EER) of 2.22% and Bo

With the continuous progress and development in the field of Internet technology, the area of medical image processing has also developed along with it. Specially, digital watermarking technology plays an essential role in the field of medical image processing and greatly improves the security of medical image information. A medical image watermarking algorithm based on an accelerated-KAZE discrete cosine transform (AKAZE-DCT) is proposed to address the poor robustness of medical image watermarking algorithms to geometric attacks, which leads to low security of the information contained in medical images. First, the AKAZE-DCT algorithm is used to extract the feature vector of the medical image and then combined with the perceptual hashing technique to obtain the feature sequence of the medical image; then, the watermarking image is encrypted with logistic chaos dislocation to get the encrypted watermarking image, which ensures the security of the watermarking information; finally, the watermarking is embedded and extracted with the zero-watermarking technique. The experimental results show that the algorithm can effectively extract the watermark under conventional and geometric attacks, reflecting better robustness and invisibility, and has certain practicality in the medical field compared with other algorithms.

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.

Stream-based biometric authentication using a novel approach based on spiking neural networks (SNNs) is addressed. SNNs have proven advantages regarding energy consumption and they are a perfect match with some proposed neuromorphic hardware chips, which can lead to a broader adoption of user device applications of artificial intelligence technologies. One of the challenges when using SNNs is the discriminative training of the network since it is not straightforward to apply the well-known error backpropagation (EBP), massively used in traditional artificial neural networks (ANNs). A network structure based on neuron columns is proposed, resembling cortical columns in the human cortex, and a new derivation of error backpropagation for the spiking neural networks that integrate the lateral inhibition in these structures. The potential of the proposed approach is tested in the task of inertial gait authentication, where gait is quantified as signals from Inertial Measurement Units (IMU), and the authors' approach to state-of-the-art ANNs is compared. In the experiments, SNNs provide competitive results, obtaining a difference of around 1% in half total error rate when compared to state-of-the-art ANNs in the context of IMU-based gait authentication.

To solve the problem of poor robustness of existing traditional DCT-based medical image watermarking algorithms under geometric attacks, a novel deep learning-based robust zero-watermarking algorithm for medical images is proposed. A Residual-DenseNet is designed, which took low-frequency features after discrete cosine transformation of medical images as labels and applied skip connections and a new objective function to strengthen and extract high-level semantic features that can effectively distinguish different medical images and binarise them to get robust hash vectors. Then, these hash vectors are bound with the chaotically encrypted watermark to generate the corresponding keys to complete the generation of watermark. The proposed algorithm neither modified the original medical image in the watermark generation stage nor required the original medical image in the watermark extraction stage. Moreover, the proposed algorithm is also suitable for multiple watermarks. Experimental results show that the proposed algorithm has good robust performance under both conventional and geometric attacks.

Face recognition has become one of the most important modalities of biometrics in recent years. It widely utilises deep learning computer vision tools and adopts large collections of unconstrained face images of celebrities for training. Such choice of the data is related to its public availability when existing document compliant face image collections are hardly accessible due to security and privacy issues. Such inconsistency between the training data and deploy scenario may lead to a leak in performance in biometric systems, which are developed specifically for dealing with ID document compliant images. To mitigate this problem, we propose to regularise the training of the deep face recognition network with a specific sample mining strategy, which penalises the samples by their estimated quality. In addition to several considered quality metrics in recent work, we also expand our deep learning strategy to other sophisticated quality estimation methods and perform experiments to better understand the nature of quality sampling. Namely, we seek for the penalising manner (sampling character) that better satisfies the purpose of adapting deep learning face recognition for images of ID and travel documents. Extensive experiments demonstrate the efficiency of the approach for ID document compliant face images.

Christian Rathgeb, Daniel Fischer, Pawel Drozdowski, Christoph Busch. Reliable detection of doppelgängers based on deep face representations.

IET Biometrics 2022 May; 11(3):215–224. https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/bme2.12072

Morphing attacks are a form of presentation attacks that gathered increasing attention in recent years. A morphed image can be successfully verified to multiple identities. This operation, therefore, poses serious security issues related to the ability of a travel or identity document to be verified to belong to multiple persons. Previous studies touched on the issue of the quality of morphing attack images, however, with the main goal of quantitatively proofing the realistic appearance of the produced morphing attacks. The authors theorise that the morphing processes might have an effect on both, the perceptual image quality and the image utility in face recognition (FR) when compared to bona fide samples. Towards investigating this theory, this work provides an extensive analysis of the effect of morphing on face image quality, including both general image quality measures and face image utility measures. This analysis is not limited to a single morphing technique but rather looks at six different morphing techniques and five different data sources using ten different quality measures. This analysis reveals consistent separability between the quality scores of morphing attack and bona fide samples measured by certain quality measures. The authors’ study goes further to build on this effect and investigate the possibility of performing unsupervised morphing attack detection (MAD) based on quality scores. The authors’ study looks into intra- and inter-dataset detectability to evaluate the generalisability of such a detection concept on different morphing techniques and bona fide sources. The authors’ final results point out that a set of quality measures, such as MagFace and CNNIQA, can be used to perform unsupervised and generalised MAD with a correct classification accuracy of over 70%.