
Wellcome to the Applied Computer Science Testing Laboratory !
RESEARCH
Research results obtained in ACSTLab:


Domain(s): Biometrics, Iris Recognition, Signal Processing
Paper(s): CrossSensor Comparison Competition 2013,
Technical Report: CrossSensor Comparison: LG4000toLG2200
Theses, © N. PopescuBodorin, 2013:
 Using appropiate techniques developed in Applied Computer Science Testing Lab, allows upgrading LG2200 based iris recognition systems to LG4000 based iris recognition systems
with minimum losses.
 As it can be seen in the reference results for past editions of CrossSensor Comparison Competition (see Fig. 21 in the above Technicat Report),
the reference ROC curve for LG4000toLG2200 comparisons starts its descent approximately from the point (1E3, 0.97202) and continues to approximately (1E4, 0.93).
As it can be seen in the figure illustrating our results for the current editions of CrossSensor Comparison Competition (see Fig. 20 in the above Technicat Report) ,
the ROC curve obtained by us for LG4000toLG2200 comparisons starts its descent approximately from the point (1E3, 0.9905), continues to approximately (1E4, 0.9856), and finishes near (1E6, 0.9339).
Hence, at a common safety level of 1E3 FAR our solution offers a higher level of user commfort (0.9905 TAR vs. 0.97202 TAR) and at a common comfort level of 0.93 TAR, our solution offers a higher level of security (1E6 FAR vs. 1E4 FAR)

Domain(s): Biometrics, Iris Recognition;
Paper(s): The Biometric Menagerie – A Fuzzy and Inconsistent Concept
Theses, © N. PopescuBodorin, 20102013:
 In iris recognition, the concepts of sheep, goats, lambs and wolves  as proposed by Doddington and Yager in the socalled Biometric Menagerie, are at most
fuzzy and at least not quite well defined. They depend not only on the users or on their biometric templates, but also on the parameters that calibrate the iris recognition system
 Denoting some users (templates) as wolves and others as lambs is a pure subjective convention which really affects the objectivity of Biometric Menagerie as a concept.
 In a lottery, many players can win the minor prizes by partially matching the official extracted variant. Hence, we could say that the extracted variant is a wolf hunting
on lambs (the winners of the minor prizes). We could say, but we do not say that.
Excepting the pure chance, nothing aggregates the group of these winners together. In
the same manner, the odds produce the matching between one specific iris code and
many others purely by chance, meaning that the iris code space is locally too agglomerated (this agglomeration could become homogeneously present in the iris code
space), and nothing else.

Domain(s): Biometrics, Signal Processing, Iris Recognition;
Paper(s): Noise Influence on the FuzzyLinguistic Partitioning of Iris Code Space
Theses, © I.M. Motoc & C.M. Noaica, 2012:
 The set of iris codes stored or used in an iris
recognition system form an fgranular space. The fgranulation is given by identifying in the iris code space the extensions of the fuzzy concepts wolves, goats,
lambs and sheep, which together form a partitioning of the iris code space previously introduced by Doddington as the biometric menagerie.
Biometric Menagerie is fuzzy, nonstationary and highly sensitive to noise.

Domain(s): Artificial Intelligence, Iris Recognition, Optical Character Recognition
Paper(s): Examples of Artificial Perceptions in Optical Character Recognition and Iris Recognition
Theses, © N. PopescuBodorin, 20102013:
 Human learning is perception based. Therefore, in artificial intelligence, the learning process and perceptions should
not be represented and investigated independently or modeled in different simulation spaces. By analogy, I assume that artificial learning is based on the artificial perception.
 Reverse engineering the human brain is one of the most relevant tasks for all AI subdisciplines of our days and is the
only task that could make us hope we will ever succeed to endow a machine with true artificial learning capabilities.
 The belief that the perceptrons learn is widely spread today in AI community and often treated as an objective fact. However, the truth is that human learning is something much more
complex than the process called by Rosenblatt “learning in the perceptron”.

Domain(s): Biometrics, Iris Recognition, Logic, Artificial Intelligence
Paper(s): Combined HaarHilbert and LogGabor Based Iris Encoders
Theses, © V.E. Balas, I.M. Motoc, A. Barbulescu, 20122013:
 Combining HaarHilbert and LogGabor encoders improves iris recognition performance leading to a less ambiguous biometric
decision landscape in which the overlap between the experimental intra and interclass score distributions diminishes or even vanishes.

Domain(s): Artificial Intelligence, Neurel Networks, Iris Recognition, Biometrics;
Paper(s): Iris Codes Classification Using Discriminant and Witness Directions
Theses, © N. PopescuBodorin, 20102011:
 In iris recognition, the use of an appropiate neural network support leads to an improvement in the artificial
perception of the separation between the intra and interclass
score distributions by setting up a perspective in which the distance beetwen them becomes obvious.
 Such an appropiate neural network support relies on Discriminant and Witness Directions

Domain(s): Computational Logic, Boolean Algebra, Artificial Intelligence, Iris Recognition, Biometrics;
Paper(s): 8Valent Fuzzy Logic for Iris Recognition and Biometry
Theses, © N. PopescuBodorin, 20102011:
 Maintaining logical consistency of an iris recognition system is a matter of finding a
suitable partitioning of the input space in enrollable and
unenrollable pairs by negotiating the user comfort and the safety
of the biometric system.
 Consistent enrollment is mandatory in order to preserve system consistency.
 The fuzzy 3valent model of iris recognition is hosted by an 8valent Boolean
algebra of modulo 8 integers that represents the computational
formalization in which a biometric system (a software agent) can
achieve the artificial understanding of iris recognition in a
logically consistent manner

Domain(s): Artificial Intelligence, Neural Networks, Evolutionary Intelligent Agents, Computational Logic, Computational Intelligence, Iris Recognition,
Paper(s): Exploratory Simulation of an Intelligent Iris Verifier Distributed System
Theses, © N. PopescuBodorin, February 2011 :
 Inconsistent enrollment can change the logic of recognition from a fuzzified 2valent consistent logic of biometric certitudes to a fuzzified 3valent inconsistent possibilistic logic of biometric beliefs justified through experimentally determined probabilities, or to a fuzzified 8valent logic which is almost consistent as a biometric theory  this quality being counterbalanced by an absolutely reasonable loss in the user comfort level.
 The fuzzy 3valent logical understanding (fuzzy different, fuzzy identical, fuzzy EER interval) of iris recognition is logically inconsistent and will prove anything, sooner or later (this is the logical mechanism through which the wolves and the lambs appear/enter in a stationary/nonadaptive biometric system, which in this way exceeds the framework of Consistent Biometry).
 If in an IIVDS the logic of accepts and rejects is the Propositional Binary Logic (PBL), then the state of corresponding to the EER interval (i.e. PA&NA) is not observable for IIVDS (or in other words the IIVDS is logically controllable).
 The modal values of truth E (empty set), D (fuzzy different), I (fuzzy identical), and O (fuzzy othewise) are four elements of a Boolean algebra defined over the congruence classes within Z8 (modulo 8 integers). The intrinsic 8valent logic of this Boolean algebra is the 8vlaent formal logic language of computing with E, D, O, and I in a logically consistent manner.
 Even when simulating an Intelligent Iris Verifier Distributed System with 1441 terminals allowed to practice random enrollment, the statistical aspect of recognition is so weak that ensures for the IIVDS outstanding performance in terms of:
1E10 pessimistic odds of false accept,
1E10 pessimistic odds of false reject,
4.12E4% undecidable cases (2.7E4% cases of honest positive claims and 1.42E4% cases of honest negative claims), and a safety interval [0.3725 0.55] of width 0.1775 between the maximum reject and minimum accept scores. Hence, the IIVDS is an almost consistent iris identifier, at least.

Domain(s): Artificial Intelligence, Neural Networks, Evolutionary Intelligent Agents, Computational Logic, Computational Intelligence, Iris Recognition,
Paper(s): Learning Iris Biometric Digital Identities for Secure Authentication. A NeuralEvolutionary Perspective Pioneering Intelligent Iris Identification
Theses, © N. PopescuBodorin, January 2011:
 In Consistent Biometry there is no difference between Iris Verification and Iris Identification.
 An Intelligent Iris Verifier/Identifier stays consistent if and only if it is a nonstationary system enabled to evolve by stepping always through and to a logically consistent state.
 For an Intelligent Iris Verifier/Identifier the time is ticking when a new enrollment occurs.
 An Intelligent Iris Verifier/Identifier is consistent if and only if the histogram of alltoall comparisons can prove at least a fuzzified and consistent understanding of two words: `genuine` and `imposter`.
 Consistent Iris Recognition is a problem of binary logic or a problem of fuzzified but still consistent binary logic.
 For any inconsistent iris verifier, Monte Carlo Simulations will never be reliable in detecting upper bounds for the False Accept Rate. In Binary Logic the contradiction is explosive. From a logical point of view, trying to demonstrate an upper bound for the expansion speed of this explosion is a nonsense, and this is mainly because, in the given context, this speed is increasing with time: in a space saturated with imbricate gravitational clusters (more dense toward the mass center), the process of finding a suitable location for a new cluster to be inserted without colliding it with the other clusters that are already there, only gets harder and harder, and finally impossible.
 For an Intelligent Iris Verifier/Identifier, `evolution` means expanding a vocabulary of digital identities simultaneously with refining a consistent formal biometric theory over this vocabulary.
 The safest way to separate two classes is identifying a third class comfortably situated inbetween them. An Intelligent Iris Verifier/Identifier is an Evolutionary Nonlinear Support Vector Machine.
 Logically Consistent Iris Recognition on a global scale is a problem of computational logic, artificial intelligence, image processing, distributed evolutionary intelligent agents, and supercomputing.

Domain(s): Artificial Intelligence, Code Optimization, Iris Recognition,
Paper(s): Comparing HaarHilbert and LogGabor based iris encoders on Bath Iris Image Database
Theses, © N. PopescuBodorin, Mars 2010:
 Iris segmentation is a NP problem. Therefore, it can be optimized either for speed or for accuracy. CFIS2 is a variant of CFIS (Circular Fuzzy Iris Segementation) optimized for speed. Still, it preserves enough accuracy for obtaining good recognition results.
 HaarHilbert Encoders are more accurate than LogGabor Encoders.
 Multienrollment and the use of MDSS (MeanDeviation Similarity Score) lead to very good separation between the classes of genuine and imposter scores. Hence, multienrollment is a step further in defining what a digital identitiy realy is.
 The number FAR(MIS)  False Accept Rate at Maximum Impostoer Score  is a measure of all errors accumulated in the biometric system prior to the matching and prior to the binary encoding of iris texture. Unforced Encoding and Matching techniques are naturally unable to overcome eye image preprocessing errors.
 In certain conditions, the number POFA(mGS)  Pessimistic Odds of False Accept at minimum Genuine Score  is a performance measure for multienrollment systems.

Domain(s): Artificial Intelligence, Iris Recognition,
Paper(s): AI Challenges in Iris Recognition. Processing Tools for Bath Iris Image Database
Theses, © N. PopescuBodorin, 2010:
 Iris Recognition is and should be considered as a challenge in Artificial Intelligence. In fact, it is a topic of Pattern Recognition.

Domain(s): Iris Recognition, Time Series Analysis
Paper(s): Automatic Detection of Common LongTerm Monetary Policies on Global Exchange Market Using Gabor Analytic Phase Binary Encoder
Theses, © N. PopescuBodorin, 2010:
 The use of binary iris encoders may lead to logical inconsistencies very easily: the global exchange market provide us with an example in which the binary encodings of two very different curves are too similar for comfort (the ideea is extended in Prop.1 / pp.8 in
this paper.

Domain(s): Computational Logic, Artificial Intelligence
Paper(s): From Cognitive Binary Logic to Cognitive Intelligent Agents
Theses, © N. PopescuBodorin, 2010:
 The Cognitive Dialect is a formal logic language whose use enables a Cognitive Intelligent Agent to know its environment,
to comunicate and to use its knowledge for others and for itself.
 A Cognitive Intelligent Agent able to speak
the Cognitive Dialect is very close to selfawareness because the dialect inherits the native selfreference ambiguity of
deductive discourse written in CCBL (Computational formalization of Cognitive Binary Logic).
 The selfreference ambiguity in CCBL reffers the following situation: the truth does not depend on who is talking, and therefore, `p`
is a simbol used by us when we talk about a given propositional variable, is a simbol used by an artificial intelligent agent
when it `talks` about a given propositional variable, or is a simbol used by a propositional variable when talking about itself, all at once.
This looks a little bit strange at first sight, but it comes very naturally: the most rudimentary intelligent agent is a bit storing the truth value
of propositional variable `p`, and the next simple intelligent agent is a logical circuit: `1==>p`, telling that `p is true`, and obviously, p <==> (1 ==> p),
or in other words, as stated by the first argumentation rule of CCBL (the mirroring rule  "regula de oglindire/scufundare in CCBL" in Romanian), p <==> [1 ==> (p V 0)].
This is the beginning of the selfawareness: `p` is equivalent to `p is true`. Hence, who could say that `p is true` ? Me, you, all of us, and even `p`, and obviously, the truth value of `p` does not depend on who is talking about `p`.
If we now cease to exist, the propositional variable will continue to talk about itself (in a silent noncontradictory autoreferential deductive discourse) waiting to be heard, waiting to be discovered.
And this is the essence of CCBL: a selfreference formal deductive discourse (theory) written with and about the propositional variables of binary logic.
Therefore, I say that selfreference sentences are native and nonparadoxical in CCBL. Of course, human understanding about selfreference sentences
formulated in semantically closed languages is a different thing.

Domain(s): Iris Recognition, Artificial Intelligence, 3valent Crisp/Fuzzy Logic of Iris Segmentation
Paper(s): Cognitive Binary Logic  The Natural Unified Formal Theory of Propositional Binary Logic
Theses, © N. PopescuBodorin, 20082010:
 CCBL (Computational Cognitive Binary Logic) is a monoaxiomatic, complete (any tautology is demonstrable/provable in CCBL),
consistent/sound (there is no formula in CCBL simultaneously false and provable/demonstrable)
and semantically closed (any logical discourse obout CCBL can be written inside CCBL;
CCBL contains its own metatheory) formalization of Binary Logic.
 CCBL is a nonparadoxical theory (in CCBL any paradox is an illegal syntax / a logical nonsense).
 The Liar Paradox (LP) is decostructed in CCBL: it is not wellformed in CCBL, hence it is not wellformed
in Binary Logic (a wrong common belief is that LP would be an wellformed formula of propositional calculus that
`paradoxically` does not have a truth value).
 The only way of entering in CCBL as a paradox is through the empty subset of its vocabulary (there is no
nonempty support for paradoxical sentences in the vocabulary of CCBL).
 In CCBL, V=FORM. Any formula (any product) of CCBL theory is a propositional variable.
 CCBL gives a dual description of propositional calculus: as a theory of 2valued propositional variables,
and as a metatheory of 3valued modal states of truth: contradiction (impossible truth), contextual (possible) truth, tautology (necesary truth).

Domain(s): Iris Recognition, Artificial Intelligence, 3valent Crisp/Fuzzy Logic of Iris Segmentation
Paper(s): A Fuzzy View on kMeans Based Signal Quantization with Application in Iris Segmentation,
Exploring New Directions in Iris Recognition
Theses, © N. PopescuBodorin, 20082009:
 CFIS (Circular Iris Fuzzy Segmentation) and GAITBE (Gabor Analytic Iris Texture Binary Encoder) are both
reliable tools for experimenting iris recognition on Bath Iris Database.
 Improving iris recognition is a matter of understanding why the statistical aspect is dominant at the
intersection of imposter and genuine distributions. Minimizing the chances of False Accept depends on knowing what
is happening there. In such cases (in which the statistical aspect is dominant at the intersection of imposter
and genuine distributions), iris verification proves to be logically inconsistent because there exist at least
one comparison which matches equal chances to be or not to be a genuine or an imposter comparison.
 If a False Accept occurs, it proves that within the vocabulary of binary iris codes enrolled in the system,
there is a nonempty support for the following contradiction:
"I'm not a genuine comparison AND I am a genuine comparison".
Hence the internal logic of the biometric system is no longer consistent.
Studying why is this happening is mainly a problem of logic.







Page maintained by PhD Nicolaie PopescuBodorin,
Contact (email): bodorin # ieee.org
Last update: September 14, 2013
