AI-enabled biometrics in recruiting: Insights from marketers for managers
بیومتریک با استفاده از هوش مصنوعی در جذب نیرو: بینش بازاریابان برای مدیران-2020
Both researchers and practitioners are only in the early stages of examining and understanding the ap- plication of artificial intelligence (AI) in terms of marketing themselves as employers or the open jobs they have. AI has the potential to significantly affect how firms reach, identify, attract, and select human capital. We examine factors that can influence a job candidate’s intent to complete AI-enabled recruiting processes, especially the influence of a firm’s use of biometrics in that process. The results show that (1) social media can increase technology use motivation and AI-enabled recruiting with (2) trendiness as a first stage boundary condition and (3) biometrics as a second stage boundary condition. We contribute to marketing knowledge by identifying that for managers wanting to influence job seekers’ technology use motivation in order to increase their participation in AI-enabled recruiting; they must focus on the indirect effects of trendiness, biometrics, and their social media usage.
Keywords: AI-enabled | Biometrics | Technology use motivation | Trendiness | Recruiting | Social media usage
TAPSTROKE: A novel intelligent authentication system using tap frequencies
TAPSTROKE: رویکرد سیستم احراز هویت هوشمند با استفاده از فرکانسهای آهسته-2019
Emerging security requirements lead to new validation protocols to be implemented to recent authen- tication systems by employing biometric traits instead of regular passwords. If an additional security is required in authentication phase, keystroke recognition and classification systems and related interfaces are very promising for collecting and classifying biometric traits. These systems generally operate in time- domain; however, the conventional time-domain solutions could be inadequate if a touchscreen is so small to enter any kind of alphanumeric passwords or a password consists of one single character like a tap to the screen. Therefore, we propose a novel frequency-based authentication system, TAPSTROKE, as a prospective protocol for small touchscreens and an alternative authentication methodology for existing devices. We firstly analyzed the binary train signals formed by tap passwords consisting of taps instead of alphanumeric digits by the regular (STFT) and modified short time Fourier transformations (mSTFT). The unique biometric feature extracted from a tap signal is the frequency-time localization achieved by the spectrograms which are generated by these transformations. The touch signals, generated from the same tap-password, create significantly different spectrograms for predetermined window sizes. Finally, we conducted several experiments to distinguish future attempts by one-class support vector machines (SVM) with a simple linear kernel for Hamming and Blackman window functions. The experiments are greatly encouraging that we achieved 1.40%–2.12% and 2.01%–3.21% equal error rates (EER) with mSTFT; while with regular STFT the classifiers produced quite higher EER, 7.49%–11.95% and 6.93%–10.12%, with Hamming and Blackman window functions, separately. The whole methodology, as an expert system for protecting the users from fraud attacks sheds light on new era of authentication systems for future smart gears and watches.
Keywords: Tapstroke | Keystroke | Authentication | Biometrics | Frequency | Short time Fourier transformation | Support vector machines
Protection of bio medical iris image using watermarking and cryptography with WPT
محافظت از تصویر عنبیه بیولوژیکی پزشکی با استفاده از علامت گذاری و رمزنگاری با WPT-2019
The emerging technologies in this present world is real time biometrics which recognized a specific person in a reliable manner through their distinct biological features. The most reliable biometric identification is an iris identification. The collection of iris images can be stored in the database which is hacked by the intruders. In order to prevent these databases with watermark text, a novel hybrid method is proposed which is a combination of Wavelet Packet Transform (WPT) and cryptography. This paper presents WPT for segmenting the iris image and finding the minimum energy band where the watermark text is embedded. The watermark text is the personal information of the owner of iris. Once the watermarking is done, the cryptographic key is used to encrypt the watermarked image. This way, both the image and the watermark text are prevented in an efficient manner. The quality measures of watermarked image have been analyzed and compared with other existing techniques. The proposed technique has been analyzed with blurring, salt and pepper, JPEG, cropping, Gaussian noise, rotate, speckle noise, filter, gamma, intensity and histogram equalization noises having PSNR value increased by 3.3%, 3.6%, 4.1%, 5.3%, 7.7%, 6.1%, 11.9%, 7.7%, 14.4%, 10.7% and 10.2% respectively which effectively increased the quality of image.
Keywords: Wavelet Packet Transform (WPT) | Watermarking | Cryptography | Peak Signal to Noise Ratio (PSNR) | Mean Square Error (MSE) | Normalized Cross Correlation (NCC)
Optimized hardware accelerators for data mining applications on embedded platforms: Case study principal component analysis
شتاب دهنده سخت افزاری بهینه سازی شده برای برنامه های استخراج داده بر روی چهارچوب های embedded: مطالعه موردی تجزیه و تحلیل مؤلفه اصلی-2019
With the proliferation of mobile, handheld, and embedded devices, many applications such as data min- ing applications have found their way into these devices. However, mobile devices have stringent area and power limitations, high speed-performance, reduced cost, and time-to-market requirements. Furthermore, applications running on mobile devices are becoming more complex requiring high processing power. These design constraints pose serious challenges to the embedded system designers. In order to pro- cess the applications on mobile and embedded systems, effectively and efficiently, optimized hardware architectures are needed. We are investigating the utilization of FPGA-based customized hardware to ac- celerate embedded data mining applications including handwritten analysis and facial recognition. For these biometric applications, Principal Component Analysis (PCA) is applied initially, followed by similar- ity measure. In this research work, we introduce novel and efficient embedded hardware architectures to accelerate the PCA computation. PCA is a classic technique to reduce the dimensionality of data by transforming the original data set into a new set of variables called Principal Components (PCs) that rep- resent the key features of the data. We propose two hardware versions for PCA computation, each with its unique optimization techniques to enhance the performance of our designs, and one specifically with additional techniques to reduce the memory access latency of embedded platforms. To the best of our knowledge, we could not find similar work for PCA, specifically catered to the embedded devices, in the published literature. We perform experiments to evaluate the feasibility and efficiency of our designs us- ing a benchmark dataset for biometrics. Our embedded hardware designs are generic, parameterized, and scalable; and achieve 78 times speedup as compared to its software counterparts
Keywords: Data mining | Dimensionality reduction techniques | Embedded and mobile systems | FPGAs | Hardware acceleration | Principal Component Analysis
Preserving privacy in speaker and speech characterisation
حفظ حریم خصوصی در شخصیت پردازی و گفتار-2019
Speech recordings are a rich source of personal, sensitive data that can be used to support a plethora of diverse applications, from health profiling to biometric recognition. It is therefore essential that speech recordings are adequately protected so that they cannot be misused. Such protection, in the form of privacy-preserving technologies, is required to ensure that: (i) the biometric profiles of a given individual (e.g., across different biometric service operators) are unlinkable; (ii) leaked, encrypted biometric information is irreversible, and that (iii) biometric references are renewable. Whereas many privacy-preserving technologies have been developed for other biometric characteristics, very few solutions have been proposed to protect privacy in the case of speech signals. Despite privacy preservation this is now being mandated by recent European and international data protection regulations. With the aim of fostering progress and collaboration between researchers in the speech, biometrics and applied cryptography communities, this survey article provides an introduction to the field, starting with a legal perspective on privacy preservation in the case of speech data. It then establishes the requirements for effective privacy preservation, reviews generic cryptography-based solutions, followed by specific techniques that are applicable to speaker characterisation (biometric applications) and speech characterisation (non-biometric applications). Glancing at non-biometrics, methods are presented to avoid function creep, preventing the exploitation of biometric information, e.g., to single out an identity in speech-assisted health care vspeaker characterisation. In promoting harmonised research, the article also outlines common, empirical evaluation metrics for the assessment of privacy-preserving technologies for speech data.
Keywords: Data privacy | Voice biometrics | Standardisation | Cryptography | Legislation
Based blockchain-PSO-AES techniques in finger vein biometrics: A novel verification secure framework for patient authentication
روش های مبتنی بر بلاکچین-PSO-AES در بیومتریک رگ های انگشت: یک چارچوب تأیید صحت جدید برای احراز هویت بیمار-2019
The main objective of this study is to propose a novel verification secure framework for patient authentication between an access point (patient enrolment device) and a node database. For this purpose, two stages are used. Firstly, we propose a new hybrid biometric pattern model based on a merge algorithm to combine radio frequency identification and finger vein (FV) biometric features to increase the randomisation and security levels in pattern structure. Secondly, we developed a combination of encryption, blockchain and steganography techniques for the hybrid pattern model. When sending the pattern from an enrolment device (access point) to the node database, this process ensures that the FV biometric verification system remains secure during authentication by meeting the information security standard requirements of confidentiality, integrity and availability. Blockchain is used to achieve data integrity and availability. Particle swarm optimisation steganography and advanced encryption standard techniques are used for confidentiality in a transmission channel. Then, we discussed how the proposed framework can be implemented on a decentralised network architecture, including access point and various databases node without a central point. The proposed framework was evaluated by 106 samples chosen from a dataset that comprises 6000 samples of FV images. Results showed that (1) high-resistance verification framework is protected against spoofing and brute-force attacks; most biometric verification systems are vulnerable to such attacks. (2) The proposed framework had an advantage over the benchmark with a percentage of 55.56% in securing biometric templates during data transmission between the enrolment device and the node database.
Keywords: Finger vein | Blockchain | Cryptography | Steganography | RFID | CIA
Is biometrics the answer to crypto-currency crime?
آیا بیومتریک جواب جرم رمزنگاری ارزی است؟-2019
Crypto-currency – commonly defined as digital assets that use cryptography to secure transactions without the need for a central banking authority – is rising in popularity and being widely adopted across the globe. According to research by the University of Cambridge1, some 3 million people are estimated to be actively trading in crypto-currencies today, and many are already using crypto to pay for items such as hotels, games and even their rent.
Parallel score fusion of ECG and fingerprint for human authentication based on convolution neural network
همجوشی امتیاز موازی ECG و اثر انگشت را برای احراز هویت انسان بر اساس شبکه های عصبی کانولوشن-2019
Biometrics have been extensively used in the past decades in various security systems and have been deployed around the world. However, all unimodal biometrics have their own limitations and disadvantages (e.g., fingerprint suffers from spoof attacks). Most of these limitations can be addressed by designing a multimodal biometric system, which deploys over one biometric modality to improve the performance and make the system robust to spoof attacks. In this paper, we proposed a secure multimodal biometric system by fusing electrocardiogram (ECG) and fingerprint based on convolution neural network (CNN). To the best of our knowledge, this is the first study to fuse ECG and fingerprint using CNN for human authentication. The feature extraction for individual modalities are performed using CNN and then biometric templates are generated from these features. After that, we have applied one of the cancelable biometric techniques to protect these templates. In the authentication stage, we proposed a Q-Gaussian multi support vector machine (QG-MSVM) as a classifier to improve the authentication performance. Dataset augmentation is successfully used to increase the authentication performance of the proposed system. Our system is tested on two databases, the PTB database from PhysioNet bank for ECG and LivDet2015 database for the fingerprint. Experimental results show that the proposed multimodal system is efficient, robust and reliable than existing multimodal authentication algorithms. According to the advantages of the proposed system, it can be deployed in real applications
Keywords: Authentication | CNN | ECG | Fingerprint | Multimodal biometrics | MSVM
An ensemble learning approach to lip-based biometric verification, with a dynamic selection of classifiers
یک رویکرد یادگیری گروه برای تأیید بیومتریک مبتنی بر لب ، با انتخاب پویای طبقه بندی کننده ها-2019
Machine learning approaches are largely focused on pattern or object classification, where a combination of several classifier systems can be integrated to help generate an optimal or suboptimal classification decision. Multiple classification systems have been extensively developed because a committee of clas- sifiers, also known as an ensemble, can outperform the ensemble’s individual members. In this paper, a classification method based on an ensemble of binary classifiers is proposed. Our strategy consists of two phases: (1) the competence of the base heterogeneous classifiers in a pool is determined, and (2) an ensemble is formed by combining those base classifiers with the greatest competences for the given input data. We have shown that the competence of the base classifiers can be successfully calculated even if the number of their learning examples was limited. Such a situation is particularly observed with biomet- ric data. In this paper, we propose a new biometric data structure, the Sim coefficients, along with an efficient data processing technique involving a pool of competent classifiers chosen by dynamic selection.
Keywords: Lip-based biometrics | Dynamic classifiers selection | Pattern recognition | Ensemble classification | Person verification
Secure key agreement based on ordered biometric features
توافق نامه كليد امن براساس ويژگي هاي بيومتريك سفارش داده شده-2019
In this work, we propose a novel secure key agreement protocol, Secure Key Agreement using Pure Or- dered Biometrics (SKA-POB), in which the cryptographic keys are generated using an ordered set of bio- metrics, without any extra shared secret data or keys. The proposed approach is instantiated using iris biometrics. Our protocol makes use of hash functions and HMAC (Hash-based Message Authentication Code) as the only cryptographic primitives; thus, it is not cryptographically resource-hungry. We also propose and integrate a window-based comparison strategy and a window reset method in SKA-POB. This way, performance is maximized without sacrificing security. Furthermore, we propose an intelligent fake block generation and distribution strategy to hide the genuine blocks in transit, which increases the resistence of our proposed protocol against correlation attacks. SKA-POB protocol works in round manner, allowing to successfully terminate with key establishment as early as possible so that the complexity is reduced for both client and server sides. Additionally, we employ multi-criteria analyses for our proposed SKA-POB protocol and we provide verification results in terms of performance analysis together with randomness, distinctiveness and attack complexity through security analysis. Results show that highly random and computationally secure keys can be generated with almost no error and with very low com- plexity.
Keywords: Biometrics | Bio-cryptography | Iris | Key agreement | Security analysis