Uncertainty Theories Based Iris Recognition System

The performance and robustness of the iris-based recognition systems still suffer from imperfection in the biometric information. This paper makes an attempt to address these imperfections and deals with important problem for real system. We proposed a new method for iris recognition system based on uncertainty theories to treat imperfection iris feature. Several factors cause different types of degradation in iris data such as the poor quality of the acquired pictures, the partial occlusion of the iris region due to light spots, or lenses, eyeglasses, hair or eyelids, and adverse illumination and/or contrast. All of these factors are open problems in the field of iris recognition and affect the performance of iris segmentation, its feature extraction or decision making process, and appear as imperfections in the extracted iris feature. The aim of our experiments is to model the variability and ambiguity in the iris data with the uncertainty theories. This paper illustrates the importance of the use of this theory for modeling or/and treating encountered imperfections. Several comparative experiments are conducted on two subsets of the CASIA-V4 iris image database namely Interval and Synthetic. Compared to a typical iris recognition system relying on the uncertainty theories, experimental results show that our proposed model improves the iris recognition system in terms of Equal Error Rates (EER), Area Under the receiver operating characteristics Curve (AUC) and Accuracy Recognition Rate (ARR) statistics.

Probability-Possibility Theories Based Iris Biometric Recognition System

The performance and robustness of the iris-based recognition systems still suffer from imperfection in the biometric information. This paper makes an attempt to address these imperfections and deals with important problem for real system. We proposed a new method for iris recognition system based on uncertainty theories to treat imperfection iris feature. Several factors cause different types of degradation in iris data such as the poor quality of the acquired pictures, the partial occlusion of the iris region due to light spots, or lenses, eyeglasses, hair or eyelids, and adverse illumination and/or contrast. All of these factors are open problems in the field of iris recognition and affect the performance of iris segmentation, its feature extraction or decision making process, and appear as imperfections in the extracted iris feature. The aim of our experiments is to model the variability and ambiguity in the iris data with the uncertainty theories. This paper illustrates the importance of the use of this theory for modeling or/and treating encountered imperfections. Several comparative experiments are conducted on two subsets of the CASIA-V4 iris image database namely Interval and Synthetic. Compared to a typical iris recognition system relying on the uncertainty theories, experimental results show that our proposed model improves the iris recognition system in terms of Equal Error Rates (EER), Area Under the receiver operating characteristics Curve (AUC) and Accuracy Recognition Rate (ARR) statistics

Uncertainty Theories Based Iris Recognition System

The performance and robustness of the iris-based recognition systems still suffer from imperfection in the biometric information. This paper makes an attempt to address these imperfections and deals with important problem for real system. We proposed a new method for iris recognition system based on uncertainty theories to treat imperfection iris feature. Several factors cause different types of degradation in iris data such as the poor quality of the acquired pictures, the partial occlusion of the iris region due to light spots, or lenses, eyeglasses, hair or eyelids, and adverse illumination and/or contrast. All of these factors are open problems in the field of iris recognition and affect the performance of iris segmentation, its feature extraction or decision making process, and appear as imperfections in the extracted iris feature. The aim of our experiments is to model the variability and ambiguity in the iris data with the uncertainty theories. This paper illustrates the importance of the use of this theory for modeling or/and treating encountered imperfections. Several comparative experiments are conducted on two subsets of the CASIA-V4 iris image database namely Interval and Synthetic. Compared to a typical iris recognition system relying on the uncertainty theories, experimental results show that our proposed model improves the iris recognition system in terms of Equal Error Rates (EER), Area Under the receiver operating characteristics Curve (AUC) and Accuracy Recognition Rate (ARR) statistics

Uncertainty Theories Based Iris Recognition System

The performance and robustness of the iris-based recognition systems still suffer from imperfection in the biometric information. This paper makes an attempt to address these imperfections and deals with important problem for real system. We proposed a new method for iris recognition system based on uncertainty theories to treat imperfection iris feature. Several factors cause different types of degradation in iris data such as the poor quality of the acquired pictures, the partial occlusion of the iris region due to light spots, or lenses, eyeglasses, hair or eyelids, and adverse illumination and/or contrast. All of these factors are open problems in the field of iris recognition and affect the performance of iris segmentation, its feature extraction or decision making process, and appear as imperfections in the extracted iris feature. The aim of our experiments is to model the variability and ambiguity in the iris data with the uncertainty theories. This paper illustrates the importance of the use of this theory for modeling or/and treating encountered imperfections. Several comparative experiments are conducted on two subsets of the CASIA-V4 iris image database namely Interval and Synthetic. Compared to a typical iris recognition system relying on the uncertainty theories, experimental results show that our proposed model improves the iris recognition system in terms of Equal Error Rates (EER), Area Under the receiver operating characteristics Curve (AUC) and Accuracy Recognition Rate (ARR) statistics.

Probability-Possibility Theories Based Iris Biometric Recognition System

The performance and robustness of the iris-based recognition systems still suffer from imperfection in the biometric information. This paper makes an attempt to address these imperfections and deals with important problem for real system. We proposed a new method for iris recognition system based on uncertainty theories to treat imperfection iris feature. Several factors cause different types of degradation in iris data such as the poor quality of the acquired pictures, the partial occlusion of the iris region due to light spots, or lenses, eyeglasses, hair or eyelids, and adverse illumination and/or contrast. All of these factors are open problems in the field of iris recognition and affect the performance of iris segmentation, its feature extraction or decision making process, and appear as imperfections in the extracted iris feature. The aim of our experiments is to model the variability and ambiguity in the iris data with the uncertainty theories. This paper illustrates the importance of the use of this theory for modeling or/and treating encountered imperfections. Several comparative experiments are conducted on two subsets of the CASIA-V4 iris image database namely Interval and Synthetic. Compared to a typical iris recognition system relying on the uncertainty theories, experimental results show that our proposed model improves the iris recognition system in terms of Equal Error Rates (EER), Area Under the receiver operating characteristics Curve (AUC) and Accuracy Recognition Rate (ARR) statistics

Possibilistic modeling of iris system for high recognition reliability

International audienceThe human iris is attracting a lot of attention leading to an increased demand for a robust and reliable iris-based recognition system. However, typical systems have random performances which can be efficient for some contexts and not for others. Generally, in real-world applications, iris data imperfections affect the performance of the recognition algorithms. Possibilistic modeling is a powerful tool to handle data imperfections or redundancy without being affected by data variability. Therefore, in this paper, we propose a new method for biometric recognition of human irises based on possibilistic modeling approach. The proposed approach is based on possibility theory concepts for modeling iris features in a possibilistic space. Therefore, a set of iris features, extracted from image samples, is transformed into possibility distributions for possibilistic iris representation. Features in the possibilistic space allow better recognition rates over conventional features. Validation of the proposed method was done on four challenging CASIA iris image databases namely Thousand, Interval, Twins and Synthetic. For illustration, we consider a typical iris system, from the literature. Such a system leads to an Accuracy Recognition Rate (ARR) of 72.27% on the CASIA Thousand iris database, an ARR of 78.06% on the CASIA Interval database, an ARR of 71.88% on the CASIA twins and an ARR of 77.35% on the CASIA synthetic database. Possibilistic modeling of features leads to an improvement of the ARR which is respectively of 99.92%, 99.94%, 99.91% and 99.83% for the considered databases. Possibilistic modeling leads as well to a significant reduction of the Error Equal Error Rate (EER). Indeed the typical system leads to an EER of 27.72%, 21.93%, 28.11% and 22.64%, respectively on the CASIA Thousands, Interval, Twins and synthetic databases. whereas our proposed system leads to EER of 0.04%, 0.05%, 0.08% and 0.16%, respectively for the above mentioned databases. Besides, the proposed method improves the system performance with respect to False Acceptance Rate (FAR), False rejection Rate (FRR), Area Under the receiver operating characteristics Curve (AUC)

Possibilistic modeling of iris system for high recognition reliability

International audienceThe human iris is attracting a lot of attention leading to an increased demand for a robust and reliable iris-based recognition system. However, typical systems have random performances which can be efficient for some contexts and not for others. Generally, in real-world applications, iris data imperfections affect the performance of the recognition algorithms. Possibilistic modeling is a powerful tool to handle data imperfections or redundancy without being affected by data variability. Therefore, in this paper, we propose a new method for biometric recognition of human irises based on possibilistic modeling approach. The proposed approach is based on possibility theory concepts for modeling iris features in a possibilistic space. Therefore, a set of iris features, extracted from image samples, is transformed into possibility distributions for possibilistic iris representation. Features in the possibilistic space allow better recognition rates over conventional features. Validation of the proposed method was done on four challenging CASIA iris image databases namely Thousand, Interval, Twins and Synthetic. For illustration, we consider a typical iris system, from the literature. Such a system leads to an Accuracy Recognition Rate (ARR) of 72.27% on the CASIA Thousand iris database, an ARR of 78.06% on the CASIA Interval database, an ARR of 71.88% on the CASIA twins and an ARR of 77.35% on the CASIA synthetic database. Possibilistic modeling of features leads to an improvement of the ARR which is respectively of 99.92%, 99.94%, 99.91% and 99.83% for the considered databases. Possibilistic modeling leads as well to a significant reduction of the Error Equal Error Rate (EER). Indeed the typical system leads to an EER of 27.72%, 21.93%, 28.11% and 22.64%, respectively on the CASIA Thousands, Interval, Twins and synthetic databases. whereas our proposed system leads to EER of 0.04%, 0.05%, 0.08% and 0.16%, respectively for the above mentioned databases. Besides, the proposed method improves the system performance with respect to False Acceptance Rate (FAR), False rejection Rate (FRR), Area Under the receiver operating characteristics Curve (AUC)

Probability-Possibility Theories Based Iris Biometric Recognition System

The performance and robustness of the iris-based recognition systems still suffer from imperfection in the biometric information. This paper makes an attempt to address these imperfections and deals with important problem for real system. We proposed a new method for iris recognition system based on uncertainty theories to treat imperfection iris feature. Several factors cause different types of degradation in iris data such as the poor quality of the acquired pictures, the partial occlusion of the iris region due to light spots, or lenses, eyeglasses, hair or eyelids, and adverse illumination and/or contrast. All of these factors are open problems in the field of iris recognition and affect the performance of iris segmentation, its feature extraction or decision making process, and appear as imperfections in the extracted iris feature. The aim of our experiments is to model the variability and ambiguity in the iris data with the uncertainty theories. This paper illustrates the importance of the use of this theory for modeling or/and treating encountered imperfections. Several comparative experiments are conducted on two subsets of the CASIA-V4 iris image database namely Interval and Synthetic. Compared to a typical iris recognition system relying on the uncertainty theories, experimental results show that our proposed model improves the iris recognition system in terms of Equal Error Rates (EER), Area Under the receiver operating characteristics Curve (AUC) and Accuracy Recognition Rate (ARR) statistics