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Proceedings ArticleDOI

At-a-distance person recognition via combining ocular features

TL;DR: This research presents a framework that extracts multiple features from iris and periocular regions from near infrared images captured at a distance of 2 meters or more and yields state-of-the-art results.
Abstract: Person recognition is a challenging research problem particularly if the images are captured at a distance and only ocular region is present. In this research, we present a framework that extracts multiple features from iris and periocular regions from near infrared images captured at a distance of 2 meters or more. Using these features and random decision forest, fusion and classification is performed and verification results are reported. On CASIA V4-at-a-distance and FOCS databases, the proposed algorithm yields state-of-the-art results; particularly achieving over 61% genuine accept rate at 0.1% false accept rate on complete CASIA V4-at-a-distance database.
Citations
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Journal ArticleDOI
TL;DR: A deep feature fusion network that exploits the complementary information presented in iris and periocular regions to enhance the performance of mobile identification and requires much fewer storage spaces and computational resources than general CNNs.
Abstract: The quality of iris images on mobile devices is significantly degraded due to hardware limitations and less constrained environments. Traditional iris recognition methods cannot achieve high identification rate using these low-quality images. To enhance the performance of mobile identification, we develop a deep feature fusion network that exploits the complementary information presented in iris and periocular regions. The proposed method first applies maxout units into the convolutional neural networks (CNNs) to generate a compact representation for each modality and then fuses the discriminative features of two modalities through a weighted concatenation. The parameters of convolutional filters and fusion weights are simultaneously learned to optimize the joint representation of iris and periocular biometrics. To promote the iris recognition research on mobile devices under near-infrared (NIR) illumination, we publicly release the CASIA-Iris-Mobile-V1.0 database, which in total includes 11 000 NIR iris images of both eyes from 630 Asians. It is the largest NIR mobile iris database as far as we know. On the newly built CASIA-Iris-M1-S3 data set, the proposed method achieves 0.60% equal error rate and 2.32% false non-match rate at false match rate $=10^{-5}$ , which are obviously better than unimodal biometrics as well as traditional fusion methods. Moreover, the proposed model requires much fewer storage spaces and computational resources than general CNNs.

125 citations


Cites methods from "At-a-distance person recognition vi..."

  • ...[52] utilize the random decision forest (RDF), which is an ensemble learning method, to combine the match scores of iris and periocular biometrics....

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Journal ArticleDOI
TL;DR: Results showed that the proposed system can remarkably raise the performance of the classical algorithms used, and the GWO-voting approach has the best performance compared to other hybrid methods with the accuracy of 97.50%.
Abstract: The level of consciousness and the concentration of drivers while driving play a vital role for reducing the number of accidents. In recent decade, in-vehicle infotainment (IVI) [or in-car entertainment (ICE)] is one of the main reasons that lead to degradation of drivers performance and losing awareness. However, the impacts of some other reasons, such as drowsiness and driving fatigue, are entirely important as well. Hence, early detection of such performance degradation using different methods is a very hot research domain. To this end, the data set is collected using two different simulated driving scenarios: normal and loaded drive (17 elderly and 51 young/35 male and 33 female). This paper, therefore, concentrates on driving performance analysis using various machine learning techniques. The optimization part of the proposed methodology has two main steps. In the first step, the performances of the K-nearest neighbors (KNN), support vector machine (SVM), and naive Bayes (NB) algorithms are improved using bagging, boosting, and voting ensemble learning techniques. Afterward, four well-known evolutionary optimization algorithms [the ant lion optimizer (ALO), whale optimization algorithm (WOA), particle swarm optimization (PSO), and grey wolf optimizer (GWO)] are applied to the system for optimizing the parameters and as a result enhance the performance of whole system. The GWO-voting approach has the best performance compared to other hybrid methods with the accuracy of 97.50%. The obtained outcomes showed that the proposed system can remarkably raise the performance of the classical algorithms used.

34 citations


Cites methods from "At-a-distance person recognition vi..."

  • ...An other application of ensemble method is in person recognition [10]....

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Journal ArticleDOI
18 Feb 2019-Sensors
TL;DR: This study proposes a new method that quickly finds a rough iris box area without accurately segmenting the iris region in the input images and performs ocular recognition based on this and had a higher recognition accuracy than existing methods.
Abstract: Accurate segmentation of the iris area in input images has a significant effect on the accuracy of iris recognition and is a very important preprocessing step in the overall iris recognition process. In previous studies on iris recognition, however, the accuracy of iris segmentation was reduced when the images of captured irises were of low quality due to problems such as optical and motion blurring, thick eyelashes, and light reflected from eyeglasses. Deep learning-based iris segmentation has been proposed to improve accuracy, but its disadvantage is that it requires a long processing time. To resolve this problem, this study proposes a new method that quickly finds a rough iris box area without accurately segmenting the iris region in the input images and performs ocular recognition based on this. To address this problem of reduced accuracy, the recognition is performed using the ocular area, which is a little larger than the iris area, and a deep residual network (ResNet) is used to resolve the problem of reduced recognition rates due to misalignment between the enrolled and recognition iris images. Experiments were performed using three databases: Institute of Automation Chinese Academy of Sciences (CASIA)-Iris-Distance, CASIA-Iris-Lamp, and CASIA-Iris-Thousand. They confirmed that the method proposed in this study had a higher recognition accuracy than existing methods.

28 citations


Cites methods from "At-a-distance person recognition vi..."

  • ...[23] performed periocular recognition on ocular images captured in a long-distance NIR camera sensor environment using traditional iris recognition, a pyramid histogram of oriented gradients (PHOG), and gist methods and then performed fusion and classification using a random...

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  • ...Fusion and classification with RDF using PHOG and gist method [23] GAR of 61....

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Journal ArticleDOI
TL;DR: This article presents a periocular-assisted dynamic framework for more accurate less-constrained iris recognition, and demonstrates the effectiveness of this framework on three publicly available iris databases using within-dataset and cross- dataset performance evaluation.
Abstract: Iris recognition has emerged as one of the most accurate and convenient biometric for person identification and has been increasingly employed in a wide range of e-security applications. The quality of iris images acquired at-a-distance or under less constrained imaging environments is known to degrade the iris recognition accuracy. The periocular information is inherently embedded in such iris images and can be exploited to assist in the iris recognition under such non-ideal scenarios. Our analysis of such iris templates also indicates significant degradation and reduction in the region of interest, where the iris recognition can benefit from a similarity distance that can consider importance of different binary bits, instead of the direct use of Hamming distance in the literature. Periocular information can be dynamically reinforced, by incorporating the differences in the effective area of available iris regions, for more accurate iris recognition. This article presents such a periocular-assisted dynamic framework for more accurate less-constrained iris recognition. The effectiveness of this framework is evaluated on three publicly available iris databases using within-dataset and cross-dataset performance evaluation, e.g., improvement in the recognition accuracy of 22.9%, 10.4% and 14.6% on three databases under both the verification and recognition scenarios.

22 citations


Cites methods from "At-a-distance person recognition vi..."

  • ...[36] utilized the random decision forest (RDF), which is an ensemble learning method, to combine the match scores of iris and periocular biometrics....

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Journal ArticleDOI
TL;DR: The performance of face recognition systems can be negatively impacted in the presence of masks and other types of facial coverings that have become prevalent due to the COVID-19 pandemic, so the periocular region of the human face becomes an important biometric cue.

14 citations

References
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Journal ArticleDOI
01 Oct 2001
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Abstract: Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.

79,257 citations


"At-a-distance person recognition vi..." refers methods in this paper

  • ...Therefore, the match scores corresponding to all four features are concatenated and then classified using random decision forest (RDF) [19], [20]....

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Journal ArticleDOI
TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.
Abstract: Presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain local binary patterns, termed "uniform," are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. We derive a generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray-scale variations since the operator is, by definition, invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table. Experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns.

14,245 citations

Journal ArticleDOI
TL;DR: The performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category.
Abstract: In this paper, we propose a computational model of the recognition of real world scenes that bypasses the segmentation and the processing of individual objects or regions. The procedure is based on a very low dimensional representation of the scene, that we term the Spatial Envelope. We propose a set of perceptual dimensions (naturalness, openness, roughness, expansion, ruggedness) that represent the dominant spatial structure of a scene. Then, we show that these dimensions may be reliably estimated using spectral and coarsely localized information. The model generates a multidimensional space in which scenes sharing membership in semantic categories (e.g., streets, highways, coasts) are projected closed together. The performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category.

6,882 citations


"At-a-distance person recognition vi..." refers background in this paper

  • ...It is observed that while creating this iris code, the inner product of filter and the particular part of iris produces a result that is often close to the quantization boundary, thus resulting in an unstable value [17]....

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  • ...Index Terms— Iris, Periocular, GIST, PHOG, Score fusion, RVM, RDF...

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Journal ArticleDOI
Michael E. Tipping1
TL;DR: It is demonstrated that by exploiting a probabilistic Bayesian learning framework, the 'relevance vector machine' (RVM) can derive accurate prediction models which typically utilise dramatically fewer basis functions than a comparable SVM while offering a number of additional advantages.
Abstract: This paper introduces a general Bayesian framework for obtaining sparse solutions to regression and classification tasks utilising models linear in the parameters Although this framework is fully general, we illustrate our approach with a particular specialisation that we denote the 'relevance vector machine' (RVM), a model of identical functional form to the popular and state-of-the-art 'support vector machine' (SVM) We demonstrate that by exploiting a probabilistic Bayesian learning framework, we can derive accurate prediction models which typically utilise dramatically fewer basis functions than a comparable SVM while offering a number of additional advantages These include the benefits of probabilistic predictions, automatic estimation of 'nuisance' parameters, and the facility to utilise arbitrary basis functions (eg non-'Mercer' kernels) We detail the Bayesian framework and associated learning algorithm for the RVM, and give some illustrative examples of its application along with some comparative benchmarks We offer some explanation for the exceptional degree of sparsity obtained, and discuss and demonstrate some of the advantageous features, and potential extensions, of Bayesian relevance learning

5,116 citations

Journal ArticleDOI
TL;DR: A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence, which implies a theoretical "cross-over" error rate of one in 131000 when a decision criterion is adopted that would equalize the false accept and false reject error rates.
Abstract: A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person's face is the detailed texture of each eye's iris. The visible texture of a person's iris in a real-time video image is encoded into a compact sequence of multi-scale quadrature 2-D Gabor wavelet coefficients, whose most-significant bits comprise a 256-byte "iris code". Statistical decision theory generates identification decisions from Exclusive-OR comparisons of complete iris codes at the rate of 4000 per second, including calculation of decision confidence levels. The distributions observed empirically in such comparisons imply a theoretical "cross-over" error rate of one in 131000 when a decision criterion is adopted that would equalize the false accept and false reject error rates. In the typical recognition case, given the mean observed degree of iris code agreement, the decision confidence levels correspond formally to a conditional false accept probability of one in about 10/sup 31/. >

3,399 citations


"At-a-distance person recognition vi..." refers methods in this paper

  • ...We first explain feature extraction from iris images and periocular images, followed by fusion and classification using random decision forest....

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  • ...Index Terms— Iris, Periocular, GIST, PHOG, Score fusion, RVM, RDF...

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