dataesr/scientific-paragraphs-categorization huggingface dataset:
Definitions and related works A biometric authentication scheme is realized into two phases. One (or several) feature vector is extracted from a biometric data during the enrollment process and is stored into a biometric database. During the authentication phase, a second feature vector is extracted from a biometric data and these two vectors are compared. It is expected that feature vectors x and x of the same individual, extracted from two measurements are sufficiently close to verify D A (x, x ) < τ A. This paper uses the Euclidean distance for D A , defined for feature vectors x, x ∈ M A = R N by: D A (x, x ) = N i=1 (x i -x i ) 2. Definition 2. 2. Let B = {x i } i=1,. ,n be a biometric database, composed of feature vectors. A biometric authentication scheme for B is an algorithm that takes a feature vector x and a threshold τ A as inputs and returns T rue if ∃i between 1 and n such that V (x, x i , τ A ) returns T rue and F alse otherwise.
In the first case, x is successfully authenticated by x i. The quality of a biometric database is estimated with two rates: the False Match Rate (FMR) that provides the percentage of impostors authenticated by the system and the False Non Match Rate (FNMR) that provides the percentage of genuine peoples rejected by the system. These rates depend on τ A and in case of equality these rates correspond to the Equal Error Rate (EER). A biometric database contains some feature vectors that strongly influence these indicators. Doddington et al. [9]  sorted them in 4 categories: sheep, goats, lambs and wolves, where wolves are able to usurp many users, causing false acceptances. These individuals appear to have generic characteristics. Similarly, Yager et al. [41]  described 4 categories of animals representing the relationship between genuine and impostor match scores: worms, chameleons, phantoms and doves, where chameleons combine both high genuine and impostor match scores.
In these categories, chameleons obtain scores belonging to the first quarter of the best scores. Ine et al. defined a wolf as a feature vector that has a successful authentication probability greater than the FMR and evaluate the security of a given database with the notion of Wolf Attack Probability (WAP) in  [36]. Countermeasures against this attack is investigated by Inuma et al. in  [15]  and tested on fingerprints by Murakami et al. in  [20]. Wolf attacks have been realized on the voice by Okhi et al. in  [24] , then by Ohki and Otsuka  [25]. Recently, Roy et al. investigated masterprints, partial fingerprints that usurp a large number of users  [29]  in a multi-enrollment process. They worked with small sensors of smartphones which acquire a lot of partial minutiae-based feature vectors.
It was later improved with machine learning based approaches in  [31, 30, 4]  and recently applied on face recognition in  [23]. For security reasons, feature vectors are not always directly stored in the database, but are used as input of a cancelable biometric transformation. These transformations were first proposed by Ratha et al. for face recognition  [27]. There are many works on cancelable transformations, as presented in the surveys  [28, 22]  on biometric templates protection. Definition 2. 3. Let K be a token (seed) space and (M B , D B ) be a metric space. A cancelable biometric scheme is a set of algorithms (T , V), where • T takes a secret seed s ∈ K, and a feature vector x ∈ M A as input, and returns a biometric template u = T (s, x) ∈ M B. • V takes two biometric templates u = T (s, x), u = T (s, x ), and a threshold τ B as input, and returns T rue if D B (u, u ) < τ B , and F alse otherwise.
Definition 2. 4. Let D = {u i } i=1,. ,n be a cancelable biometric database, composed of biometric templates u i = T (s i , x i ). A cancelable biometric authentication scheme for D is an algorithm that takes a biometric template u = T (s, x) and a threshold τ B as inputs and returns T rue if ∃i between 1 and n such that s = s i and V(u, T (s i , x i ), τ B ) returns T rue and F alse otherwise. In the first case, x is successfully authenticated by x i. It is expected that the cancelable biometric transformation T does not significantly decrease the performance of the original biometric system, in terms of EER. This paper uses the Hamming distance for D B , defined for biometric templates u, u ∈ M B = {0, 1} M by: D B (u, u ) = M i=1 u i ⊕ u i , where ⊕ is exclusive or (xor) bitwise operator.
Biometric masterkeys A biometric masterkey x is a synthetic feature vector such that the corresponding biometric template u is close to several biometric templates in the database. The first part of this section is a generalization of concepts of the previous section (wolves, masterprints) to cancelable biometric database. The second part proposes an alternative by constructing a cancelable biometric database that matches with a given masterkey. In this case, the cancelable transformation is introduced to provide such masterkeys efficiently. Definition 3. 1. Let D = {u i } i=1,. ,n be a cancelable biometric database and τ B a threshold. x is a masterkey for D, by respect to τ B , if ∀i between 1 and n, V(T (s i , x), T (s i , x i ), τ B ) = T rue, with T (s i , x i ) = u i. Obviously, x is a masterkey for the database {T (s, x)}, but the research of a masterkey for a large database is not necessarily an easy task.
A less restrictive goal is the research of a minimal number of masterkeys such that all templates of the database are close to at less one template corresponding to one masterkey. Definition 3. 2. Let D = {u i } i=1,. ,n be a cancelable biometric database and τ B a threshold. D is said covered by a set of r masterkeys {x 1 ,. x r } by respect to τ B if ∀i between 1 and n, ∃k between 1 and r such that V(T (s i , x k ), T (s i , x i ), τ B ) = T rue. The minimum number R such that D is covered by a set of R masterkeys is called optimal dictionary size. In the previous definition, each masterkey x i ∈ {x 1 ,. x r } is a masterkey for a subset D i ⊂ D and we have ∪ r i=1 D i = D. Nevertheless, the number of templates in each subset D i is generally different. Definition 3. 3. A cancelable biometric database D = {u i } i=1,.
,n is said -covered by a masterkey x, with 0 < ≤ 1 if there exists a subset D of D of n persons such that x is a masterkey for D. The optimal coverage percentage of D is the maximal number E such that D is E-covered by a masterkey x ∈ M A. Such a set of masterkeys can be used for attacks on the cancelable biometric database, similarly to the work of Roy et al. on minutiae  [29] , called dictionary attack, using a set of carefully chosen masterkeys. Nevertheless these masterkeys could provide other applications. For example, a masterkey could be used for a system of delegation of biometric authentication, with the possibility for one person to be authenticated as herself and also as other person, similarly to proxy signatures  [19]  or group signatures  [6]  in cryptography. A second approach of the biometric masterkey problem is the construction of a cancelable biometric database from a feature vector x and n feature vectors extracted from n peoples, such that x is a masterkey for the cancelable database.
It means that, in this second approach, the seeds used by peoples are constructed, with the knowledge of x, contrary to the previous approach. Definition 3. 4. Let x be a feature vector, τ B be a threshold, and B = {x i } i=1,. ,n be a biometric database. A cancelable biometric database D is said compliant with x by respect to τ B if there exists s 1 ,. s n such that D = {T (s i , x i )} and x is a masterkey for D by respect to τ B. This second approach uses not the cancelable biometric transformation for security but uses it for the construction of masterkeys. It makes possible to adjust the choice of seeds to allow super access, i. e. , a vector usurping the entire base. This can allow an administrator to have the same access as its users without adding another authentication channel. If an insecure transformation is used, an additional security layer should be added in the template storage.
It can also allow an attacker active in the enrollment phase to build a backdoor in the authentication system, without adding suspicious code or making the database dubious, as in the following definition. Definition 3. 5. Let B = {x i } i=1,. ,n a biometric database and D = {T (s i , x i )} a cancelable biometric database compliant with a masterkey x. Then x is a backdoor if there exists no polynomial algorithm that distinguishes s 1 , T (x 1 , s 1 ),. s n , T (x n , s n ) from s 1 , T (x 1 , s 1 ),. s n , T (x n , s n ), where s 1 ,. s n are randomly generated from the set of seeds (x is unknown). In this paragraph, we consider the theoretical complexity to find masterkeys. We suppose that the metric space (M B , D B ) is {0, 1} M with the Hamming distance and that the biometric transformation T is pseudorandom.
Thus, given a biometric template u, a feature vector x and a threshold τ , the probability p that the distance D B (T (x, s), u) < τ for a random seed s is p = 1 2 M τ -1 i=0 M i. Let D = {u i } i=1,. ,n be a cancelable biometric database of n binary templates. A feature vector x, such that D is -covered by x, is expected after 1/p n trials. Let B = {x i } i=1,. ,n be a biometric database of n feature vectors and x a feature vector. A set of n seeds s 1 ,. s n , such that the corresponding cancelable biometric database D is compliant with x (Definition 3. 4) is expected after n/p trials. Thus, the research of a masterkey given a biometric database is clearly easier than given a cancelable database.
Biometric Databases The first biometric database is the FVC2002 database  [18] , which contains t = 8 biometric images of n = 100 peoples obtained from fingerprints. The second one is the sub LFW database  [14]  used in Dong et al. experiments  [10] , which contains t = 10 biometric images of n = 158 peoples obtained from face pictures. This database is noted LFW10 in this section. A third database, named LFW8, is extracted from LFW10 by taking the first 100 peoples, with the first 8 biometric images, for comparison with the FVC2002 database. Feature vectors are extracted from FVC using Gabor filters  [3]. Each biometric feature vector is composed of N = 512 real values of 64 bits and the EER of the biometric database is 10% with a threshold τ A = 240. 7. Feature vectors extracted from LFW are obtained using the deep network InsightFace  [8].
Masterkeys from Cancelable Biometric Database The cancelable biometric transformation used in this subsection is the biohashing algorithm, mapping R N into {0, 1} M where M = 128, described in Section 2. The binary size of the seed is 128 bits. After transformation the EER of the cancelable biometric database is around 16. 5% with a threshold τ B = 17 for FVC2002 and around 1. 9% with a threshold τ B = 51 for LFW10 and LFW8. The research of a masterkey, as presented in Definition 3. 1, from a cancelable biometric database uses genetic algorithm where the fitness function is not the number of people who are authenticated by the masterkey, because it does not provide the best results. Two other fitness functions have been investigated, the first one based on the average of the minimal Hamming distance obtained for each person and the second one based on the sum of the minimal Hamming distance of not authenticated peoples by the masterkey.
The second fitness function (sum) provides the best results on both databases and is used in next experiments. We only consider the first biometric template for each person of the cancelable biometric database during the evaluation of the best optimal coverage percentage and for the best optimal dictionary size. Table  1  gives the best optimal coverage percentage (OCP), as described in Definition 3. 3, and the best optimal dictionary size (ODS), as described in Definition 3. The genetic algorithm is processed on a population of 200 peoples during 500 iterations. Figure  1  shows that coverage does not increase from 350 iterations. Vulnerabilities of cancelable transformations in the stolen token scenario have been already studied. Typically, nearby-feature preimages have been already constructed on biohashing in this scenario with various methods.
Nevertheless, it is the first time that these attacks are extended to the entire database by the construction of masterkeys. The strategy uses a genetic algorithm which is not related to the weaknesses of this transformation. Typically, specific techniques as linear optimization algorithms give efficient results for nearby-feature preimages on biohashing and could be used to improve the performance of our algorithm. Nevertheless, the choice of this algorithm depends on the cancelable transformation, whereas the research of masterkeys should be possible for all transformations.
Masterkeys from Biometric Database In this second scenario, we have a biometric database and a feature vector, called candidate masterkey. The objective is the generation of seeds for which the feature vector is a masterkey for the corresponding cancelable biometric database as presented in Definition 2. 4. In this scenario the transformation is chosen for the efficiency of masterkeys creation, not for data protection. Thus, the beginning of this subsection describes the choice of the transformation and its parameters. The performance of random projection-based schemes are analyzed using the Johnson-Lindenstauss Lemma  [16, 7]  which establishes that for any 0 < < 1, there exists a map f : R N → R M such that for all x, y in a subset of n points of R N with M ≥ O( -2 log n) (1 -) x -y 2 2 ≤ f (x) -f (y) 2 2 ≤ (1 + ) x -y 2 2.
The two following random projection N × M matrices are proposed by Achlioptas in  [1]. In the first case, called JL1, coefficients of the projection matrix M s are 1/ √ M with a probability 1/2 -1/ √ M with a probability 1/2 In the second case, called JL2, coefficients of M s are    3/M with a probability 1/6 0 with a probability 2/3 -3/M with a probability 1/6 The main advantage of these transformations is the low computational time for matrix generation compared to the Gram-Schmidt algorithm. The value of in the previous inequality is detailed in figure  2  for N = 512 and M between 32 and 256 using the FVC database (a random database also provides similar curves). Two transformations are used: the map composed of a random orthonormalized matrix (Gram-Schmidt), where decreases from 0.
9 to 0. 5 and for the two previous transformations (Achlioptas) which provides the similar results ( 0. 1 for M ≥ 128). The previous experiments on has very few consequences on the EER. The EER of the cancelable database is close to the EER of the original database in figure  3  for both transformations for M ≥ 128. Even if the value of in the previous experiment is different, in the case of Achlioptas's transformations, the threshold is stable, whereas in the first transformation, the threshold increases. Thus, the research of masterkeys from a biometric database seems more interesting, directly in the Euclidean space, because the EER does not change compared to the original database if M is not too small (for M = 128, around 11% against 10% on FVC and around 1% against 0. 2% for LFW). Nevertheless, the computational time for the research of masterkeys is not negligible, particularly for some feature vectors.
The EER of the cancelable database obtained with the two previous transformations and a binarization step is a little bit increased compared to the original EER: around 16. 5% for FVC and 2. 1% for LFW in figure  4  with M = 128. Consequently we investigate the research of masterkeys with transformations T : R N → R M or {0, 1} M , defined by the Achlioptas transformations, ending or not by a binarization step. Random matrices are generated from a seed of 16 bytes as previously. Let B = {x j i } i=1,. ,n,j=1. ,t be a biometric database, composed of feature vectors (n peoples with t vectors by people). In experiments, the candidate masterkey is a feature vector of a chosen people of a database. The research of seeds for a cancelable biometric database compliant with a masterkey candidate x j i , as presented in Definition 3.
4 is realized, taking one by one the (n -1) j th feature vectors of each person, and choice seed, randomly generated from {0, 1} 128 , so that the candidate masterkey is successfully authenticated. Experiments use the following algorithm, and outputs an array of n -1 seeds for each candidate masterkey, with the threshold at EER. The seed research is only limited by a value c max , in the case of the FVC database without binarization of the transformation. If a successful seed is not founded after c max trials, the output of the algorithm is F ailure (in most of experiments, c max is not achieved).
Algorithm 1 Research of seeds for masterkeys Require: B the database, τ B the threshold, c max SeedT = [] for i = 1 to n and j = 1 to t do The research of seeds for masterkeys is successful for all feature vectors from FVC and LFW10 (called now LFW), for both Achlioptas's transformations (called JL1 and JL2), with the binarization step. Figure  5  presents the number of clock required for each feature vector for both transformations on FVC database (more precisely, it is an average of the t vectors of each person), and the same results for LFW. It shows that the time is more important for some people of the FVC database, compared to the LFW database. It also shows that the second transformation provides slightly better results in both database (but the number of distance comparison is similar).
The research of seeds is very efficient and only takes a few seconds for almost masterkey candidates. SeedL = [] for k = 1 to n, with k = i do cpt ← 0, s k ← rand(1, 2 128 ) while V(T (s k , x j i ), T (s k , x j k ), τ B ) = F alse and cpt < c max do s k ← rand(1, 2 128 ) end while if V(T (s k , x j i ), T (s k , x j k ), τ B ) = T The average of the EER in these n * t cancelable biometric databases is around 17% for FVC (for both transformations) which is close to the EER of a cancelable database obtained with random seeds (around 16. 5% for both transformations). For LFW, the average of the EER is around 2. 4 % which is also close to the EER of a cancelable database obtained with random seeds (around 2. 1% for both transformations). If these transformations do not use a binarization step, the time of research increases a lot for the FVC database (more precisely for some people of this database), so we don't detail it.
In the case of LFW database, the number of clock is not significantly increased compared to the number of clock required with the binarization step. Figure  6  presents the number of clock during the research of the masterkeys for LFW (more precisely, it is an average of the t vectors of each person). In this case, the second transformation is again more efficient than the first one. The EER of these n * t databases is around 1. 3% which is close to the EER of the LFW database obtained with random seeds (around 1% for both transformations without binarization). In this subsection, masterkeys have been efficiently founded for LFW in the Euclidean space. Nevertheless, due to complexity for some vectors of the FVC database, it seems more appropriate to use a transformation with binarization in the case where acquisition of feature vectors has not been perfectly realized.
Multiple masterkeys In this subsection, a variant of the previous scenario is investigated. The previous definition ensures for the owner's masterkey that the masterkey is successfully authenticated with enrollment data of other people. Nevertheless it does not ensure that another biometric data of the owner's masterkey is authenticated on this database. This subsection studies the percentage of the cancelable database cov-ered by these other feature vectors. Let B = {x j i } i=1,. ,n,j=1. ,t be a biometric database, composed of feature vectors (n peoples with t vectors by people). Let x 1 ,. x t be feature vectors which are candidates for masterkey (x 1 ,. x t are assumed from the same people, but it doesn't mean that the distance between them is always lower than the used threshold). In a first time seeds are computed for a subset x t1 ,.
x t T , called first set (if such a seed is not founded, the seed minimizing the worst distance is kept). In a second time, we look for the coverage percentage of the database D provided by the t -T other vectors, called second set. The corresponding templates of x 1 ,. , x t , noted u 1 ,. , u t , form the first set and the second set of templates, depending to the feature vectors. Experiments only use the JL2 transformation, with and without binarization, on FVC and LFW, for T = 1 and T = 4. For practical reasons, we used a time limit of 5 minutes in place of c max. The number of templates in D matching with the first set of T templates u t1 ,. u t T (in blue for T = 1 and green for T = 4) and with the second set of t -T other templates (in orange for T = 1 and red for T = 4) is presented in Figure  7  for FVC the Hamming space and in Figure  8  for LFW the Hamming and Euclidean space.
For example, with T = 1 in FVC, around 60% of templates from the second set of templates match with at less 40 templates of D (orange curve), whereas with T = 4, around 60% of templates from the second set of templates match with at less 75 templates of D (red curve). Moreover we have only one blue point in these figures, because 100% of templates of the first set match with all templates in the database, as seen in the previous subsection (if T = 4 it is not the case because the limit of 5 minutes for each seed computation).
Conclusion and Future Work This paper has introduced the concept of biometric masterkeys in two scenarios. In the first one, there exists a cancelable biometric database, and we try to find a feature vector for which the corresponding template matches with the greatest number of templates of the database. It is a formalization and a generalization of Wolf Attacks and masterprints on fingerprints, experimented with the biohashing algorithm. In the second scenario, there exists a biometric database, and we try to find a set of seeds for which the corresponding template of a given feature vector matches with the greatest number of templates of the database. The main contribution of this work is the construction of such a cancelable biometric database covered by one of its feature vectors, with an EER similar to the original cancelable database.
The research of masterkeys in this second scenario is completely different from previous works on wolves or masterprints, because it uses the transformation, not for data protection, but for the research of preimages. This work presents new opportunities for a biometric system administrator, to construct a cancelable biometric database which matches with a template obtained from its own feature vector. This masterkey only requires a public seed by template and is very easy to construct. There are several future works and improvements of this paper, particularly in the research of seeds, providing good coverage percentage of the cancelable database for other acquisitions of feature vectors. Other applications of masterkeys, as biometric systems with fine-grained access controls, will also be investigated in the future.
From a user perspective, immersive content can elicit more intense emotions than flat-screen presentations. From a system perspective, efficient storage and distribution remain challenging, and must consider user attention. Understanding the connection between user attention, user emotions and immersive content is therefore key. In this article, we present a new dataset, PEM360 of user head movements and gaze recordings in 360°videos, along with self-reported emotional ratings of valence and arousal, and continuous physiological measurement of electrodermal activity and heart rate. The stimuli are selected to enable the spatiotemporal analysis of the connection between content, user motion and emotion. We describe and provide a set of software tools to process the various data modalities, and introduce a joint instantaneous visualization of user attention and emotion we name Emotional maps.