The voting as a way to increase the decision reliability

Zuev, Yu. A.; Ivanov, Stefan

doi:10.1016/s0016-0032(98)00025-8

Cited by 34 publications

(15 citation statements)

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“…Each biometric system makes its own recognition decision based on its own feature vector. A majority vote scheme [15] can be used to make the final recognition decision. The integration at the feature extraction level assumes a strong interaction among the input measurements and such schemes are referred to as tightly coupled integrations [31].…”

Section: ) Fusion At the Matching Score (Confidence Or Rank) Levelmentioning

confidence: 99%

An Introduction to Biometric Recognition

Jain

Ross

Prabhakar³

2004

IEEE Trans. Circuits Syst. Video Technol.

3,801

1,616

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Abstract-A wide variety of systems requires reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user and no one else. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. In the absence of robust personal recognition schemes, these systems are vulnerable to the wiles of an impostor. Biometric recognition or, simply, biometrics refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. By using biometrics, it is possible to confirm or establish an individual's identity based on "who she is," rather than by "what she possesses" (e.g., an ID card) or "what she remembers" (e.g., a password). In this paper, we give a brief overview of the field of biometrics and summarize some of its advantages, disadvantages, strengths, limitations, and related privacy concerns.

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Section: ) Fusion At the Matching Score (Confidence Or Rank) Levelmentioning

confidence: 99%

An Introduction to Biometric Recognition

Jain

Ross

Prabhakar³

2004

IEEE Trans. Circuits Syst. Video Technol.

3,801

1,616

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show abstract

“…One can talk about inter-modal combination [39,51,5,41,42,23,16,15], e.g. combination of face with iris, and intra-modal combination [1,53,31,54,17,36,32,55], e.g., combination of outputs of two classifiers on the same modality, or the combination of outputs of two different sensors, such as IR and visible [9,8] and visible and 3D [6][7][8], on the same modality. In Table 1 we summarize the work in computer-vision based multi-modal biometric combination.…”

Section: Introductionmentioning

confidence: 99%

Outdoor recognition at a distance by fusing gait and face

Liu

Sarkar

2007

Image and Vision Computing

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We explore the possibility of using both face and gait in enhancing human recognition at a distance performance in outdoor conditions. Although the individual performance of gait and face based biometrics at a distance under outdoor illumination conditions, walking surface changes, and time variations are poor, we show that recognition performance is significantly enhanced by combination of face and gait. For gait, we present a new recognition scheme that relies on computing distances based on selected, discriminatory, gait stances. Given a gait sequence, covering multiple gait cycles, it identifies the salient stances using a population hidden Markov model (HMM). An averaged representation of the detected silhouettes for these stances are then built using eigenstance shape models. Similarity between two gait sequences is based on the similarities of these averaged representations of the salient stances. This gait recognition strategy, which essentially emphasizes shape over dynamics, significantly outperforms the HumanID Gait Challenge baseline algorithm. For face, which is a mature biometric for which many recognition algorithms exists, we chose the elastic bunch graph matching based face recognition method. This method was found to be the best in the FERET 2000 studies. On a gallery database of 70 individuals and two probe sets: one with 39 individuals taken on the same day and the other with 21 individuals taken at least 3 months apart, results indicate that although the verification rate at 1% false alarm rate of individual biometrics are low, their combination performs better. Specifically, for data taken on the same day, individual verification rates are 42% and 40% for face and gait, respectively, but is 73% for their combination. Similarly, for the data taken with at least 3 months apart, the verification rates are 48% and 25% for face and gait, respectively, but is 60% for their combination. We also find that the combination of outdoor gait and one outdoor face per person is superior to using two outdoor face probes per person or using two gait probes per person, which can considered to be statistical controls for showing improvement by biometric fusion.

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“…In this sense, multimodal biometrics is a conventional decision fusion problem -to combine evidence provided by each biometrics to improve the overall decision accuracy. Generally, multiple evidences may be integrated at one of the following three different levels [2]: (/) Abstract level; the output from each module is only a set of possible labels without any confidence value associated with the labels; in this case, the simple majority rule may be employed to reach a more reliable decision [20] (ii) Rank level; the output from each module is a set of possible labels ranked by decreasing confidence values, but the confidence values themselves are not specified; (Hi) Measurement level; the output from each module is a set of possible labels with associated confidence values; in this case, more accurate decisions can be made by integrating different confidence values. Dieckmann et al [6] have proposed an abstract level fusion scheme: "2-from-3 approach" which integrates face, lip motion, and voice based on the principle that a human uses multiple clues to identify a person.…”

Section: Multimodal Biometrics For Verificationmentioning

confidence: 99%

Multimodal Biometrics

Lin¹,

Jain²

Biometrics

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The voting as a way to increase the decision reliability

Cited by 34 publications

References 0 publications

An Introduction to Biometric Recognition

An Introduction to Biometric Recognition

Outdoor recognition at a distance by fusing gait and face

Multimodal Biometrics

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