Biometric Algorithm Research Articles

On the security risk of pre-image attack on cancelable biometrics

Over the years, a number of biometric template protection schemes, often based on the notion of “cancelable biometrics”, have been proposed. An ideal cancelable biometric algorithm complies with four criteria: irreversibility, revocability, unlinkability, and performance preservation. Cancelable biometrics employs an irreversible but distance preserving transformation to convert the original biometric templates into protected templates. Matching in the transform domain can be accomplished due to the property of distance preservation. However, distance preservation also entails security issues, a point often overlooked in existing research. In this paper, we have conducted a comprehensive security analysis of distance preservation in cancelable biometrics for the first time. The analysis is based on a pre-image attack, which is launched to break the security of cancelable biometrics under Kerckhoffs’s assumption. Furthermore, we propose a general security analysis framework under the single and cross-transformation attacks, which also employs an information leakage estimation strategy based on mutual information as a complement. The experimental results performed on real face, iris, and fingerprint data demonstrate that the risks originating from the matching scores computed from the distance/similarity of two cancelable templates greatly compromise the security of cancelable biometric schemes, including the classic Biohashing, Index-of-max hashing, Non-linear multi-dimensional spectral hashing, Indexing-First-One hashing, Bloom Filter and Two-factor Protected Minutia Cylinder-Code. The security versus accuracy trade-off is discussed and recommendations for designing a biometric system secure against pre-image attacks are also proposed. The source code is available at github.com/biometricsecurity/CBrisks.

Development of a facial recognition model using optimized convolution neural network with Tiny-ML and fingerprint authentication for safe lock entry

Safe boxes have been used for centuries to protect valuable items, and their design and construction have evolved over time to improve security. In this study, we carried out the design and implementation of a facial recognition technology using optimized convolutional neural networks (CNNs) with TinyML and fingerprint. The facial recognition system is designed using pre-trained CNN architectures then optimized with TinyML to make it fit into resource constrained edge device, the fingerprint recognition is designed using ridge matching biometric algorithm which fits in perfectly into the microcontroller-based system. The safe box was fabricated using sheet metal and insulated using heat resistive material (fiber glass), components like OV2640 camera module, ESP32-S3, R305 Fingerprint sensor, 4.3 inches touch screen TFT display and a charging module to manage and charge the systems battery were used. The system was implemented to be a level two security and two-way verification system using secured Wi-Fi to access the system before the facial and fingerprint recognition functionality is activated, the sequence of operation can either be facial recognition before fingerprint recognition or fingerprint recognition before facial recognition. Evaluating the system’s facial recognition performance with confusion matrix, of all the optimized CNN architectures like VGGNet, SENet, RestNet, MobileNet-V2, EfficientNet-B0, SENet Architecture performed best outputting an accuracy of 98%, precision of 92%, also SENet unoptimized model performed same, MobileNet-V2 performed optimally in the inference speed test with a speed of 15ms/images with the fingerprint also evaluated using confusion matrix and perform with 98% accuracy. It was noticed that the inference speed of the unoptimized model is faster than that of the TinyML optimized model which is justifiable based on the processing clock speed, memory usage, GPU and architecture of the microcontroller/microprocessor. From the result obtained TinyML proves to be suitable mean for edge AI (artificial intelligence) and is recommended for future research and development.

User authentication system based on human exhaled breath physics.

This work, in a pioneering approach, attempts to build a biometric system that works purely based on the fluid mechanics governing exhaled breath. We test the hypothesis that the structure of turbulence in exhaled human breath can be exploited to build biometric algorithms. This work relies on the idea that the extrathoracic airway is unique for every individual, making the exhaled breath a biomarker. Methods including classical multi-dimensional hypothesis testing approach and machine learning models are employed in building user authentication algorithms, namely user confirmation and user identification. A user confirmation algorithm tries to verify whether a user is the person they claim to be. A user identification algorithm tries to identify a user's identity with no prior information available. A dataset of exhaled breath time series samples from 94 human subjects was used to evaluate the performance of these algorithms. The user confirmation algorithms performed exceedingly well for the given dataset with over 97% true confirmation rate. The machine learning based algorithm achieved a good true confirmation rate, reiterating our understanding of why machine learning based algorithms typically outperform classical hypothesis test based algorithms. The user identification algorithm performs reasonably well with the provided dataset with over 50% of the users identified as being within two possible suspects. We show surprisingly unique turbulent signatures in the exhaled breath that have not been discovered before. In addition to discussions on a novel biometric system, we make arguments to utilise this idea as a tool to gain insights into the morphometric variation of extrathoracic airway across individuals. Such tools are expected to have future potential in the area of personalised medicines.

A methodology for detecting digitally morphed photographic portraits based on an artifact segmentation model

Problem statement. Existing biometric systems (hereinafter - BS) are based on the measurement of physiological and anatomical distinguishing characteristics of people. The BS compares the presented biometric samples (photographs) and determines the degree of similarity, which is used to make a decision on admission to the object or to classified information. One of the types of attacks on biometric presentation, against which no system is fully protected, is the method of morphing. It is based on the creation of an artificial biometric sample from two original ones, which retains the characteristics of both at a level sufficient to pass the biometric algorithm with high degrees of similarity. The purpose of the study. To develop a methodology for detecting digital morphed photographic portraits based on the artifact segmentation model for solving biometric identification problems. Results. A methodology for detecting digital morphed photographic portraits based on an artefact segmentation model in RGB colour space is proposed. Practical significance. The obtained methodology can be used in the BS before the biometric algorithm in order to protect the system from the corresponding type of attacks on these systems.

Privacy-Friendly Datasets of Synthetic Fingerprints for Evaluation of Biometric Algorithms

The datasets of synthetic biometric samples are created having in mind two major objectives: bypassing privacy concerns and compensating for missing sample variability in datasets of real biometric samples. If the purpose of generating samples is the evaluation of biometric systems, the foremost challenge is to generate so-called mated impressions—different fingerprints of the same finger. Note that for fingerprints, the finger’s identity is given by the co-location of minutiae points. The other challenge is to ensure the realism of generated samples. We solve both challenges by reconstructing fingerprints from pseudo-random minutiae making use of the pix2pix network. For controlling the identity of mated impressions, we derive the locations and orientations of minutiae from randomly created non-realistic synthetic fingerprints and slightly modify them in an identity-preserving way. Our previously trained pix2pix models reconstruct fingerprint images from minutiae maps, ensuring that the realistic appearance is transferred from training to synthetic samples. The main contribution of this work lies in creating and making public two synthetic fingerprint datasets of 500 virtual subjects with 8 fingers each and 10 impressions per finger, totaling 40,000 samples in each dataset. Our synthetic datasets are designed to possess characteristics of real biometric datasets. Thus, we believe they can be applied for the privacy-friendly testing of fingerprint recognition systems. In our evaluation, we use NFIQ2 for approving the visual quality and Verifinger SDK for measuring the reconstruction success.

One-Factor Cancellable Fingerprint Template Protection Based on Index Self-Encoding

The existing one-factor cancellable biometrics algorithms generally require random sequences to reorder the biometrics, which reduces the discrimination of the transformed biometrics. Some algorithms hide and transmit the random sequence by XORing the random sequence with original biometrics, which may cause the leakage of the original biometrics. Therefore, this paper proposes a one-factor cancellable fingerprint template protection based on index self-encoding. First, the integer sequence generated by the hash function is used as the index. The random sequence is automatically encoded directly by the index value, and the generated binary sequence retains the original biological characteristics to the greatest extent. Second, self-encoding binary sequence and random binary sequence are XORed to obtain the encoded key without directly storing binary factor sequences. Experiments are implemented on the fingerprint database of FVC2002 and FVC2004, the results show that the recognition rate is enhanced; meanwhile, it fits the design criteria of cancellable biometrics.

Automated segmentation and measurement of the female pelvic floor from the mid-sagittal plane of 3D ultrasound volumes.

Transperineal ultrasound (TPUS) is a valuable imaging tool for evaluating patients with pelvic floor disorders, including pelvic organ prolapse (POP). Currently, measurements of anatomical structures in the mid-sagittal plane of 2D and 3D US volumes are obtained manually, which is time-consuming, has high intra-rater variability, and requires an expert in pelvic floor US interpretation. Manual segmentation and biometric measurement can take 15 min per 2D mid-sagittal image by an expert operator. An automated segmentation method would provide quantitative data relevant to pelvic floor disorders and improve the efficiency and reproducibility of segmentation-based biometric methods. Develop a fast, reproducible, and automated method of acquiring biometric measurements and organ segmentations from the mid-sagittal plane of female 3D TPUS volumes. Our method used a nnU-Net segmentation model to segment the pubis symphysis, urethra, bladder, rectum, rectal ampulla, and anorectal angle in the mid-sagittal plane of female 3D TPUS volumes. We developed an algorithm to extract relevant biometrics from the segmentations. Our dataset included 248 3D TPUS volumes, 126/122 rest/Valsalva split, from 135 patients. System performance was assessed by comparing the automated results with manual ground truth data using the Dice similarity coefficient (DSC) and average absolute difference (AD). Intra-class correlation coefficient (ICC) and time difference were used to compare reproducibility and efficiency between manual and automated methods respectively. High ICC, low AD and reduction in time indicated an accurate and reliable automated system, making TPUS an efficient alternative for POP assessment. Paired t-test and non-parametric Wilcoxon signed-rank test were conducted, with p<0.05 determining significance. The nnU-Net segmentation model reported average DSC and p values (in brackets), compared to the next best tested model, of 87.4% (<0.0001), 68.5% (<0.0001), 61.0% (0.1), 54.6% (0.04), 49.2% (<0.0001) and 33.7% (0.02) for bladder, rectum, urethra, pubic symphysis, anorectal angle, and rectal ampulla respectively. The average ADs for the bladder neck position, bladder descent, rectal ampulla descent and retrovesical angle were 3.2mm, 4.5mm, 5.3mm and 27.3°, respectively. The biometric algorithm had an ICC>0.80 for the bladder neck position, bladder descent and rectal ampulla descent when compared to manual measurements, indicating high reproducibility. The proposed algorithms required approximately 1.27 s to analyze one image. The manual ground truths were performed by a single expert operator. In addition, due to high operator dependency for TPUS image collection, we would need to pursue further studies with images collected from multiple operators. Based on our search in scientific databases (i.e., Web of Science, IEEE Xplore Digital Library, Elsevier ScienceDirect and PubMed), this is the first reported work of an automated segmentation and biometric measurement system for the mid-sagittal plane of 3D TPUS volumes. The proposed algorithm pipeline can improve the efficiency (1.27 s compared to 15 min manually) and has high reproducibility (high ICC values) compared to manual TPUS analysis for pelvic floor disorder diagnosis. Further studies are needed to verify this system's viability using multiple TPUS operators and multiple experts for performing manual segmentation and extracting biometrics from the images.

Usability study of various biometric techniques in bank branches

The purpose of the presented research was to evaluate the performance of the prepared biometric algorithms and obtain information on the opinions and preferences of their users in bank branches. The study aimed to determine users' attitudes towards particular modalities and preferences on how to use biometrics after the bank customers had practical experience with the operation of the prototype solutions. The research results allow a better understanding of users' needs and expectations, which can help to increase the knowledge of the need for biometric solutions in banking. Making appropriate changes based on the research results can help improve user satisfaction. The experiments included the collection of biometric sample collection and verification processes conducted by bank customers assisted by employees. The study collected biometric samples and questionnaires from 365 people, and 179 complete verifications were conducted. About 95% of those surveyed expressed acceptance of the implemented solutions for handling transactions using biometrics. The HandVein-FaceImage (hand vein scanning and face recognition) modality pair was rated highest in 70% of cases in the studied population.

Exploring Bias in Sclera Segmentation Models: A Group Evaluation Approach

Bias and fairness of biometric algorithms have been key topics of research in recent years, mainly due to the societal, legal and ethical implications of potentially unfair decisions made by automated decision-making models. A considerable amount of work has been done on this topic across different biometric modalities, aiming at better understanding the main sources of algorithmic bias or devising mitigation measures. In this work, we contribute to these efforts and present the first study investigating bias and fairness of sclera segmentation models. Although sclera segmentation techniques represent a key component of sclera-based biometric systems with a considerable impact on the overall recognition performance, the presence of different types of biases in sclera segmentation methods is still underexplored. To address this limitation, we describe the results of a group evaluation effort (involving seven research groups), organized to explore the performance of recent sclera segmentation models within a common experimental framework and study performance differences (and bias), originating from various demographic as well as environmental factors. Using five diverse datasets, we analyze seven independently developed sclera segmentation models in different experimental configurations. The results of our experiments suggest that there are significant differences in the overall segmentation performance across the seven models and that among the considered factors, ethnicity appears to be the biggest cause of bias. Additionally, we observe that training with representative and balanced data does not necessarily lead to less biased results. Finally, we find that in general there appears to be a negative correlation between the amount of bias observed (due to eye color, ethnicity and acquisition device) and the overall segmentation performance, suggesting that advances in the field of semantic segmentation may also help with mitigating bias.

Score-Level Fusion of 3D Face and 3D Ear for Multimodal Biometric Human Recognition.

A novel multimodal biometric system is proposed using three-dimensional (3D) face and ear for human recognition. The proposed model overcomes the drawbacks of unimodal biometric systems and solves the 2D biometric problems such as occlusion and illumination. In the proposed model, initially, the principal component analysis (PCA) is utilized for 3D face recognition. Thereafter, the iterative closest point (ICP) is utilized for 3D ear recognition. Finally, the 3D face is fused with a 3D ear using score-level fusion. The simulations are performed on the Face Recognition Grand Challenge database and the University of Notre Dame Collection F database for 3D face and 3D ear datasets, respectively. Experimental results reveal that the proposed model achieves an accuracy of 99.25% using the proposed score-level fusion. Comparative analyses show that the proposed method performs better than other state-of-the-art biometric algorithms in terms of accuracy.

Neuromanagement decision making in facial recognition biometric authentication as a mobile payment technology in retail, restaurant, and hotel business models

Research background: With growing evidence of biometric identification techniques as authentication, there is a pivotal need for comprehending contactless payments by use of facial recognition algorithms in retail, restaurant, and hotel business models. Purpose of the article: In this research, previous findings were cumulated showing that harnessing facial recognition payment applications as software-based contactless biometric algorithms results in remarkably qualitative enhancement in purchasing experience. Methods: Throughout March and November 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was carried out, with search terms including "facial recognition payment technology", "facial recognition payment system", "facial recognition payment application", "face recognition-based payment service", "facial authentication for mobile payment transactions", and "contactless payment through facial recognition algorithms". As the analyzed research was published between 2017 and 2021, only 187 articles satisfied the eligibility criteria. By removing questionable or unclear findings (limited/nonessential data), results unsubstantiated by replication, too general content, or having quite similar titles, 38, mainly empirical, sources were selected. The Systematic Review Data Repository was harnessed, a software program for the gathering, processing, and analysis of data for our systematic review. The quality of the selected scholarly sources was assessed by employing the Mixed Method Appraisal Tool. Findings &amp; value added: Harnessing facial recognition payment applications as software-based contactless biometric algorithms results in remarkably qualitative enhancement in purchasing experience. Subsequent attention should be directed to whether perceived value and trust shape customers? adoption of biometric recognition payment devices.

Cancelable Speaker Identification System Based on Optical-Like Encryption Algorithms

Biometric authentication is a rapidly growing trend that is gaining increasing attention in the last decades. It achieves safe access to systems using biometrics instead of the traditional passwords. The utilization of a biometric in its original format makes it usable only once. Therefore, a cancelable biometric template should be used, so that it can be replaced when it is attacked. Cancelable biometrics aims to enhance the security and privacy of biometric authentication. Digital encryption is an efficient technique to be used in order to generate cancelable biometric templates. In this paper, a highly-secure encryption algorithm is proposed to ensure secure biometric data in verification systems. The considered biometric in this paper is the speech signal. The speech signal is transformed into its spectrogram. Then, the spectrogram is encrypted using two cascaded optical encryption algorithms. The first algorithm is the Optical Scanning Holography (OSH) for its efficiency as an encryption tool. The OSH encrypted spectrogram is encrypted using Double Random Phase Encoding (DRPE) by implementing two Random Phase Masks (RPMs). After the two cascaded optical encryption algorithms, the cancelable template is obtained. The verification is implemented through correlation estimation between enrolled and test templates in their encrypted format. If the correlation value is larger than a threshold value, the user is authorized. The threshold value can be determined from the genuine and imposter correlation distribution curves as the midpoint between the two curves. The implementation of optical encryption is adopted using its software rather than the optical setup. The efficiency of the proposed cancelable biometric algorithm is illustrated by the simulation results. It can improve the biometric data security without deteriorating the recognition accuracy. Simulation results give close-to-zero values for the Equal Error Rate (EER) and close-to-one values for the Area under Receiver Operator Characteristic (AROC) curve.

Online Signature Verification Systems on a Low-Cost FPGA

This paper describes three different approaches for the implementation of an online signature verification system on a low-cost FPGA. The system is based on an algorithm, which operates on real numbers using the double-precision floating-point IEEE 754 format. The double-precision computations are replaced by simpler formats, without affecting the biometrics performance, in order to permit efficient implementations on low-cost FPGA families. The first approach is an embedded system based on MicroBlaze, a 32-bit soft-core microprocessor designed for Xilinx FPGAs, which can be configured by including a single-precision floating-point unit (FPU). The second implementation attaches a hardware accelerator to the embedded system to reduce the execution time on floating-point vectors. The last approach is a custom computing system, which is built from a large set of arithmetic circuits that replace the floating-point data with a more efficient representation based on fixed-point format. The latter system provides a very high runtime acceleration factor at the expense of using a large number of FPGA resources, a complex development cycle and no flexibility since it cannot be adapted to other biometric algorithms. By contrast, the first system provides just the opposite features, while the second approach is a mixed solution between both of them. The experimental results show that both the hardware accelerator and the custom computing system reduce the execution time by a factor ×7.6 and ×201 but increase the logic FPGA resources by a factor ×2.3 and ×5.2, respectively, in comparison with the MicroBlaze embedded system.

Study on the Viability of Canine Nose Pattern as a Unique Biometric Marker.

Simple SummaryThis paper shows that the canine nose pattern, which is an interlocking pattern of beads and grooves on a dog’s nose, is unique to each individual dog. For this purpose, the nose images of 60 dogs were collected at three separate times, each roughly three to four months apart. This longitudinal cohort study was designed to ensure the diversity of data, wherein dogs of diverse age, gender, and breed are well represented in the dataset. In this study, the nose patterns of these dogs were examined visually and by a biometric algorithm to determine the uniqueness of the canine nose pattern. It was found that the canine nose pattern remains invariant through the passage of time during the observation period; and that the canine nose pattern is indeed unique to each dog. Our finding confirms and enhances the claims of earlier works by others that the canine nose pattern is unique to each animal and serves as a unique biometric marker. For further study, this dataset was augmented by adding to it the nose images of 10 beagle dogs taken once every month in a ten-month period to create an enlarged dataset of 278 images of 70 dogs of 19 breeds. The study with this enlarged dataset also leads to the same conclusion.The uniqueness of the canine nose pattern was studied. A total of 180 nose images of 60 dogs of diverse age, gender, and breed were collected. The canine nose patterns in these images were examined visually and by a biometric algorithm. It was found that the canine nose pattern remains invariant regardless of when the image is taken; and that the canine nose pattern is indeed unique to each dog. The same study was also performed on an enlarged dataset of 278 nose images of 70 dogs of 19 breeds. The study of the enlarged dataset also leads to the same conclusion. The result of this paper confirms and enhances the claims of earlier works by others that the canine nose pattern is indeed unique to each animal and serves as a unique biometric marker.

The Formation and Invariance of Canine Nose Pattern of Beagle Dogs from Early Puppy to Young Adult Periods.

Simple SummaryIn this paper, we examine whether the canine nose pattern, which is an interlocking pattern of beads and grooves on a dog’s nose, is unique to each individual animal. For this purpose, the nose images of ten beagle dogs were taken every month for the ten-month period starting from month two and ending in month eleven. Six of them are siblings born of one dam and the other four of another dam. In this longitudinal study, the canine nose patterns of these ten dogs are examined visually and by a biometric algorithm to determine whether the canine nose patterns in two images of the same dog taken at different time remain the same and whether two images of different dogs are indeed different regardless of when the images are taken. It is found that the canine nose pattern of the beagle dog is fully formed at the second month after birth, that this nose pattern stays invariant, and that the canine nose pattern is indeed unique to each animal. Our finding confirms and enhances the claims of earlier works that the canine nose pattern is indeed unique to each animal, and could be used as a unique biometric marker.The formation and invariance of the canine nose pattern is studied. Nose images of ten beagle dogs were collected for ten months from month two to month eleven. The nose patterns in these images are examined visually and by a biometric algorithm. It is found that the canine nose pattern is fully formed at the end of the second month since birth and remains invariant until the end of the eleventh month. This study also strongly indicates that the canine nose pattern can be used as a unique biometric marker for each individual dog.

Biometric technologies for security of banking operations in Ukraine and foreign countrie

The issue of introduction of innovative means of consumer protection against illegal actions in the field of banking is becomingrelevant in connection with the expansion of the range of banking services in Ukraine. The article summarizes the status and prospects of using biometric identification methods in the banking sector in Ukraine and abroad. Analysis and systematization of scientific publicationsand legal framework revealed that a potential direction of the concept of customer orientation of banks is the use of biometrictechnologies in the provision of services that ensure high-quality preservation of personal data of customers. It is stated that in Ukrainethere is a need to strengthen both technological and legal mechanisms for the protection of biometric data for the needs of the bankingsector. One of the factors that significantly complicates the implementation of biometrics in banks is the lack of technical capabilities.However, there is a gradual dynamics contained in the testing of various methods of biometric identification in banking institutions.Contradictions that arise between the actual needs of the use of biometric technologies in the field of banking by the state of regu -latory and legal support in Ukraine slow down the use of effective security tools with a high degree of reliability in the banking sector.Appropriate regulatory regulation of the use of biometric technologies for identification purposes will promote the development of thesetechnologies by strengthening the confidence of consumers of banking services, as well as competition of biometric algorithms in thepresence of unified standards and requirements. Factors that shape the conditions for committing criminal offenses using biometric datacan be neutralized by effective legal means, as well as by the relevant actors in combating these offenses. It is concluded that the introductionof biometrics for the banking sector in the future in Ukraine requires taking into account the leading international experienceon these issues and an integrated approach.

Recognizing Human Races through Machine Learning—A Multi-Network, Multi-Features Study

The human face holds a privileged position in multi-disciplinary research as it conveys much information—demographical attributes (age, race, gender, ethnicity), social signals, emotion expression, and so forth. Studies have shown that due to the distribution of ethnicity/race in training datasets, biometric algorithms suffer from “cross race effect”—their performance is better on subjects closer to the “country of origin” of the algorithm. The contributions of this paper are two-fold: (a) first, we gathered, annotated and made public a large-scale database of (over 175,000) facial images by automatically crawling the Internet for celebrities’ images belonging to various ethnicity/races, and (b) we trained and compared four state of the art convolutional neural networks on the problem of race and ethnicity classification. To the best of our knowledge, this is the largest, data-balanced, publicly-available face database annotated with race and ethnicity information. We also studied the impact of various face traits and image characteristics on the race/ethnicity deep learning classification methods and compared the obtained results with the ones extracted from psychological studies and anthropomorphic studies. Extensive tests were performed in order to determine the facial features to which the networks are sensitive to. These tests and a recognition rate of 96.64% on the problem of human race classification demonstrate the effectiveness of the proposed solution.

Selecting Promising Junior Swimmers in Egypt Using Automated Biometric Algorithms of Image Processing and Fuzzy Concepts

Modern sports need a great physical and psychological effort from young athletes to reach high performance levels. Stature length, leg length, foot length, arm length, hand length, shoulder width, hip width, chest width and weight are the anthropometric characteristics that affect swimmers' performances. This paper introduces new techniques to select promising junior swimmers in Egypt. It develops two automated algorithms to select junior swimmer depending on their anthropometric measurement. The first technique uses Canny filter to develop the first algorithm, while the second one uses the Fuzzy concepts. The proposed algorithms make use of the image processing technique to handle the anthropometric measurements, by detecting the human body feature points automatically from the front and side images. The 101 feature points extract automatically from 36 human body measurements, while swimming games needs only 8 body dimensions from these 36 human body measurements. Therefore, the proposed system is not limited to swimming sports but can also be applied to other sports. Moreover, the experimental results and the corresponding statistical analysis show the high accuracy and advantages of the proposed algorithms. The first algorithm improved the results that could have obtained using the well-known fully vision-based automatic human body method by 22.23%, 15.9%,29.41%, and 27.5%, for stature length, arm length, leg length, and shoulder width, respectively. Also, it gave the best results for Leg Length, and Shoulder width, while the second one yielded the best result for Stature Length.

Method of comparison of biometric algorithms in software and hardware complexes

Method of comparison of biometric algorithms in software and hardware complexes

Multilingual Audio-Visual Smartphone Dataset and Evaluation

Smartphones have been employed with biometric-based verification systems to provide security in highly sensitive applications. Audio-visual biometrics are getting popular due to their usability, and also it will be challenging to spoof because of their multimodal nature. In this work, we present an audio-visual smartphone dataset captured in five different recent smartphones. This new dataset contains 103 subjects captured in three different sessions considering the different real-world scenarios. Three different languages are acquired in this dataset to include the problem of language dependency of the speaker recognition systems. These unique characteristics of this dataset will pave the way to implement novel state-of-the-art unimodal or audio-visual speaker recognition systems. We also report the performance of the bench-marked biometric verification systems on our dataset. The robustness of biometric algorithms is evaluated towards multiple dependencies like signal noise, device, language and presentation attacks like replay and synthesized signals with extensive experiments. The obtained results raised many concerns about the generalization properties of state-of-the-art biometrics methods in smartphones.