Biometric key generation techniques are used to reliably generate cryptographic material from biometric signals. Existing constructions require users to perform a particular activity (e.g., type or say a password, or provide a handwritten signature), and are therefore not suitable for generating keys continuously. In this paper we present a new technique for biometric key generation from free-text keystroke dynamics. This is the first technique suitable for continuous key generation. Our approach is based on a scaled parity code for key generation (and subsequent key reconstruction), and can be augmented with the use of population data to improve security and reduce key reconstruction error. In particular, we rely on linear discriminant analysis (LDA) to obtain a better representation of discriminable biometric signals. To update the LDA matrix without disclosing user's biometric information, we design a provably secure privacy-preserving protocol (PP-LDA) based on homomorphic encryption. Our biometric key generation with PP-LDA was evaluated on a dataset of 486 users. We report equal error rate around 5 when using LDA, and below 7 without LDA.
Monrose al @cite_12 evaluate the performance of BKG based on spoken password using data from 50 users. They report a false-negative rate of 4 Handwritten signature is another behavioral modality, where biometric key generation has been studied. Multiple papers, for example by Freire al @cite_10 , Feng al @cite_13 and more recently Scheuermann al @cite_18 evaluate the performance. The dataset sizes for the first three papers are 330, 25 and 144 users; the last paper does not include the number of users. The false accept false reject rates presented are 57 Physical biometrics have also been used for biometric key generation, evaluated on fingerprints by Clancy @cite_14 al, Uludag al @cite_11 , Sy and Krishnan @cite_15 and others. BKG on iris was studied by Rathgeb and Uhl @cite_2 , @cite_16 and Wu al @cite_20 , and on face images by Chen al @cite_17 .
@cite_18 @cite_14 @cite_10 @cite_20 @cite_17 @cite_2 @cite_15 @cite_16 @cite_13 @cite_12 @cite_11
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Rapid movement generation models are described in the literature as an efficient tool to apprehend the handwriting behavior. Fields of application are diverse, including handwriting description, regeneration, and more recently OCR. In this paper, we propose a grapheme-based approach to offline Arabic writer identification and verification. Rather than extracting naturel graphemes from a training corpus using segmentation and clustering, it synthesizes its own graphemes based on the beta-elliptic model. Originality lies in the independence of the grapheme codebook from any training process, and the use of a model instead. One full and four partial codebooks are generated and tested. Using feature selection, raw codebooks are reduced in size with respect to FDR, FDR and cross-correlation, and random subsampling criteria. A total of 60 feature vectors are extracted using template matching, and evaluated with 411 individual writers from the IFN ENIT database. The results presented in this study demonstrated the wide representativity and the good generalization capability of synthetic codebooks. We obtained a top1 rate=90.02 and a top5 rate=96.35 for writer identification, and an EER=2.1 for writer verification. Our approach showed better properties than most of the surveyed techniques in terms of supported corpus size and identification rates. To the best of our knowledge, this study is among the first to exploit the concept of model-based synthetic codebooks in writer identification and verification. HighlightsUniversal synthetic codebooks are used for writer identification and verification.The beta-elliptic handwriting generation model is used to create the codebooks.Neither corpus-driven training nor prior segmentation is needed in this approach.Synthetic codebook demonstrated good generalization capabilities for Arabic. We introduce hand movement, orientation, and grasp (HMOG), a set of behavioral features to continuously authenticate smartphone users. HMOG features unobtrusively capture subtle micro-movement and orientation dynamics resulting from how a user grasps, holds, and taps on the smartphone. We evaluated authentication and biometric key generation (BKG) performance of HMOG features on data collected from 100 subjects typing on a virtual keyboard. Data were collected under two conditions: 1) sitting and 2) walking. We achieved authentication equal error rates (EERs) as low as 7.16 (walking) and 10.05 (sitting) when we combined HMOG, tap, and keystroke features. We performed experiments to investigate why HMOG features perform well during walking. Our results suggest that this is due to the ability of HMOG features to capture distinctive body movements caused by walking, in addition to the hand-movement dynamics from taps. With BKG, we achieved the EERs of 15.1 using HMOG combined with taps. In comparison, BKG using tap, key hold, and swipe features had EERs between 25.7 and 34.2 . We also analyzed the energy consumption of HMOG feature extraction and computation. Our analysis shows that HMOG features extracted at a 16-Hz sensor sampling rate incurred a minor overhead of 7.9 without sacrificing authentication accuracy. Two points distinguish our work from current literature: 1) we present the results of a comprehensive evaluation of three types of features (HMOG, keystroke, and tap) and their combinations under the same experimental conditions and 2) we analyze the features from three perspectives (authentication, BKG, and energy consumption on smartphones). Biometric features are extracted from a complex pattern and stored as high dimensional data. These data do not follow traditional sorting order like numerical and alphabetical data. Hence, a linear search method makes the identification process extremely slow as well as increases the false acceptance rate beyond an acceptable range. To address this problem, we propose an efficient indexing mechanism to retrieve iris biometric templates using Gabor energy features. The Gabor energy features are calculated from the preprocessed iris texture in different scales and orientations to generate a 12-dimensional index key for an iris template. An index space is created based on the values of index keys of all individuals. A candidate set is retrieved from the index space based on the values of query index key. Next, we rank the retrieved candidates according to their occurrences. If the identity of the query template is matched, then it is a hit, otherwise a miss. We have experimented our approach with Bath, CASIA-V3-Interval, CASIA-V4-Thousand, MMU2, and WVU iris databases. Our proposed approach gives 11.3 , 14.5 , 16.3 , 13.5 , and 10.3 penetration rates and 98.2 , 91.1 , 90.7 , 85.2 , and 96 hit rates for Bath, CASIA-V3-Interval, CASIA-V4-Thousand, MMU2, and WVU iris database, respectively, when we consider the retrieving templates up to the fifth rank. Experiments substantiate that our approach is capable of retrieving biometric data with a higher hit rate and lower penetration rate compared to the existing approaches. Application of Gabor energy features to index iris data proves to be effective for fast and accurate retrieval. With our proposed approach, it is possible to retrieve a set of iris templates similar to the query template in the order of milliseconds and is independent of sizes of databases. Human users have a tough time remembering long cryptographic keys. Hence, researchers, for so long, have been examining ways to utilize biometric features of the user instead of a memorable password or passphrase, in an effort to generate strong and repeatable cryptographic keys. Our objective is to incorporate the volatility of the user's biometric features into the generated key, so as to make the key unguessable to an attacker lacking significant knowledge of the user's biometrics. We go one step further trying to incorporate multiple biometric modalities into cryptographic key generation so as to provide better security. In this article, we propose an efficient approach based on multimodal biometrics (Iris and fingerprint) for generation of secure cryptographic key. The proposed approach is composed of three modules namely, 1) Feature extraction, 2) Multimodal biometric template generation and 3) Cryptographic key generation. Initially, the features, minutiae points and texture properties are extracted from the fingerprint and iris images respectively. Subsequently, the extracted features are fused together at the feature level to construct the multi-biometric template. Finally, a 256-bit secure cryptographic key is generated from the multi-biometric template. For experimentation, we have employed the fingerprint images obtained from publicly available sources and the iris images from CASIA Iris Database. The experimental results demonstrate the effectiveness of the proposed approach.