Exploiting the error resilience of emerging data-rich applications, approximate computing promotes the introduction of small amount of inaccuracy into computing systems to achieve significant reduction in computing resources such as power, design area, runtime or energy. Successful applications for approximate computing have been demonstrated in the areas of machine learning, image processing and computer vision. In this paper we make the case for a new direction for approximate computing in the field of biometric security with a comprehensive case study of iris scanning. We devise an end-to-end flow from an input camera to the final iris encoding that produces sufficiently accurate final results despite relying on intermediate approximate computational steps. Unlike previous methods which evaluated approximate computing techniques on individual algorithms, our flow consists of a complex SW/HW pipeline of four major algorithms that eventually compute the iris encoding from input live camera feeds. In our flow, we identify overall eight approximation knobs at both the algorithmic and hardware levels to trade-off accuracy with runtime. To identify the optimal values for these knobs, we devise a novel design space exploration technique based on reinforcement learning with a recurrent neural network agent. Finally, we fully implement and test our proposed methodologies using both benchmark dataset images and live images from a camera using an FPGA-based SoC. We show that we are able to reduce the runtime of the system by 48 χ on top of an already HW accelerated design, while meeting industry-standard accuracy requirements for iris scanning systems.
A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition is regarded as the most reliable and accurate biometric identification system available. An approach for accurate Biometric Recognition and Identification of Human Iris Patterns using Neural Network has been illustrated by gopikrishnan et. al. It has been concluded by Yingzi Du et. al that the partial iris portion of the iris pattern describes the uniqueness and the pupil has no direct effect on the accuracy of the biometric recognition. In this paper the Iris recognition has been carried out employing a template of size 10 × 480 pixels instead of 20 × 480 pixels as employed in the earlier paper. The results of the two sizes of the templates have been compared and it has been observed that the accuracy of the results obtained with the limited template size is comparable with that of the one with the full size. The reason for this is also discussed in this paper. The improved methodology suggested has resulted in the reduction of the space requirement as well as time complexity with no loss in accuracy. This paper also provides results of iris recognition performed applying Hamming distance, Feed forward back propagation, Cascade forward back propagation, Elman forward back propagation and perceptron. It has been established that the method suggested applying perceptron provides the best accuracy in respect of iris reco
recognition is most precise and consistent biometric identification system accessible in the current situation. The demand for an accurate biometric system that provides dependable identification and verification of an individual has increased over the years. A biometric system that provides reliable and accurate identification of an individual is an iris recognition system. This reliability is provided by unique patterns of human iris which differs from person to person up to an extent of identical twins having different iris patterns. This paper has proposed the hybridization of Hough Circular Transform, Scale Invariant Feature Transform and Genetic Algorithm. The genetic algorithm is applied to optimize the features set as obtained by Scale invariant feature transform. KeywordsRecognition, Hough Transformation, SIFT, Genetic Algorithm.
The development of technology today has greatly influenced the development of science, one of which is in the recognition of iris patterns. When compared to fingerprints, the iris has the advantage of being protected by the eyelids and is more stable as the human age increases. The iris in human vision functions to regulate the size of the pupil and regulate the amount of light entering the eye. If observed more deeply the iris has unique characteristics of each individual. so that the iris can be used as a biometric mark for identification. Artificial Neural Network (ANN) is a tool to solve problems, especially in the field and iris pattern recognition. In general, Artificial Neural Network has a working principle that mimics the human neural network system, weighs the actions to be taken, and makes decisions like humans. Iris recognition can be used as an alternative if the introduction of fingerprints as a biometric identity fails. in this study, iris recognition uses the Dela Rule algorithm. The Delta Rule algorithm has the advantage of being able to check errors during the learning process. This will certainly make the Delta Rule algorithm have a high level of accuracy in iris pattern recognition.
Iris recognition, a relatively new biometric technology, has great advantages such as variability, stability and security, thus it is the most promising for high security environments. Among its applications are border control in airports and harbors, access control in laboratories and factories, identification for Automatic Teller Machines and restricted access to police evidence rooms. There have been several implementations of security systems using biometric, especially for identification and verification cases. The term "biometrics" is derived from the Greek words bio (life) and metric (to measure). The pattern used in the biometric is the iris pattern in human eye. The iris pattern has been proved unique for each person. A literature review of the most prominent algorithms implemented in each stage is presented. This paper provides a review of major iris I. Introduction Biometric recognition is an emerging technology which employs the physiological and behavioral characteristics to identify an individual. The physiological characteristics include the iris, fingerprint, face and hand geometry. Voice and signature are categorized as the behavioral characteristics. Among these, the human iris is an annular region between the sclera (the white portion of the eye) and the pupil (the darkest portion of the eye). Iris is gaining lots of attention due its unique pattern. The patterns that give uniqueness to the iris are the coronas, furrows, stripes and so on (1). These pattern
Humans have distinctive and unique traits which can be used to distinguish them from other humans, acting as a form of identification. A number of traits characterising physiological or behavioral characteristics of human can be used for biometric identification. Basic physiological characteristics are face, facial thermograms, fingerprint, iris, retina, hand geometry, odour/scent. Voice, signature, typing rhythm, gait are related to behavioral characteristics. The critical attributes of these characteristics for reliably recognition are the variations of selected characteristic across the human population, uniqueness of these characteristics for each individual, their immutability over time (Jain et al.,1998). Human iris is the best characteristic when we consider these attributes. The texture of iris is complex, unique, and very stable throughout life. Iris patterns have a high degree of randomness in their structure. This is what makes them unique. The iris is a protected internal organ and it can be used as an identity document or a password offering a very high degree of identity assurance. Also the human iris is immutable over time. From one year of age until death, the patterns of the iris are relatively constant (Jain et al.,1998, Adler,1965). Because of uniqueness and immutability, iris recognition is one of accurate and reliable human identification technique. Nowadays biometrics technology plays important role in public security and information security domains. Ir
techniques on video images of one or both of the irises of an individual's eyes, whose complex patterns are unique, stable, and can be seen from some distance
Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual's eyes, whose complex patterns are unique, stable, and can be seen from some distance. The discriminating powers of all biometric technologies depend on the amount of entropy they are able to encode and use in matchi
Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual's eyes, whose complex patterns are unique, stable, and can be seen from some distance. The discriminating powers of all biometric technologies depend on the amount of entropy they are able to encode and use in matching. Iris recognition is exceptional in this regard, enabling the avoidance of "collisions" (False Matches) even in cross-comparisons across massive populations. Its major limitation is that image acquisition from distances greater than a meter or two, or without cooperation, can be very…
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