This paper presents various techniques used for extracting the features of face and fingerprint b... more This paper presents various techniques used for extracting the features of face and fingerprint biometrics. More reliable recognition and performance can be achieved if both face and fingerprint modalities are fused together. The problem in multimodal biometric fusion is that the features of each modality are extracted with different algorithm which makes it difficult to fuse them together. The work presented in this paper focuses on the bimodal biometric system having Gabor filter algorithm for feature extraction as it is capable of extracting the features of both face and fingerprint biometrics. Along with this various techniques used for the fusion of two unimodal modalities have been discussed and it has been found that feature level fusion is best among all as accuracy up to 99.25% can be achieved using Gabor filter. Also the performance characteristics of all the techniques are shown on the basis of FAR (False Acceptance Rate) and FRR (False Rejection Rate) values.
International Journal of Advanced Research in Computer Science, 2017
As modern means of communication upgrade in their potential and receptiveness, they instigate add... more As modern means of communication upgrade in their potential and receptiveness, they instigate additional demands in terms of security. Biometric recognition is a new technology that has become the essence of an extensive array of highly secure identification and personal verification solutions. Unimodal biometric system suffers from inherent limitations of noise in sensor data, non universality of biometric trait, intra-class variations and unaccepted error rates. Multimodal biometric system overcome all these limitations by presenting multiple evidences of single biometric trait due to which spoofing of these simultaneously become more difficult for an imposter. In this paper, face and fingerprint are the two biometrics used to build multibiometric system. Here, fusion of two modalities is done at matching score level. An experimental result shows that the Multimodal biometric system is efficient compared to unimodal biometric system.
While multimodal biometric systems are considered to be more robust than unimodal ones but tradit... more While multimodal biometric systems are considered to be more robust than unimodal ones but traditional fusion rules are more sensitive to spoofing attempts. The proposed system is designed to overcome spoofing in worst-case scenario where impostor was able to create fake biometric traits of both face and fingerprint modalities in the presented multimodal biometric system. The paper investigates median filtering fusion rule as a spoofing resistant alternative to traditional sum rule based fusion rules. Experiments on the latest face video database (CASIA Face Anti-Spoofing Database) and fingerprint spoofing database (Fingerprint Liveness Detection Competition 2015) illustrate that the given system is more robust to spoofing attacks than the existing anti-spoofing methods even when m out of n samples of both the biometric traits to be combined are attacked. KeywordsMultimodal Biometrics, Anti-spoofing, Biometric feature extraction, Biometric score fusion.
This paper presents various techniques used for extracting the features of face and fingerprint b... more This paper presents various techniques used for extracting the features of face and fingerprint biometrics. More reliable recognition and performance can be achieved if both face and fingerprint modalities are fused together. The problem in multimodal biometric fusion is that the features of each modality are extracted with different algorithm which makes it difficult to fuse them together. The work presented in this paper focuses on the bimodal biometric system having Gabor filter algorithm for feature extraction as it is capable of extracting the features of both face and fingerprint biometrics. Along with this various techniques used for the fusion of two unimodal modalities have been discussed and it has been found that feature level fusion is best among all as accuracy up to 99.25% can be achieved using Gabor filter. Also the performance characteristics of all the techniques are shown on the basis of FAR (False Acceptance Rate) and FRR (False Rejection Rate) values.
International Journal of Advanced Research in Computer Science, 2017
As modern means of communication upgrade in their potential and receptiveness, they instigate add... more As modern means of communication upgrade in their potential and receptiveness, they instigate additional demands in terms of security. Biometric recognition is a new technology that has become the essence of an extensive array of highly secure identification and personal verification solutions. Unimodal biometric system suffers from inherent limitations of noise in sensor data, non universality of biometric trait, intra-class variations and unaccepted error rates. Multimodal biometric system overcome all these limitations by presenting multiple evidences of single biometric trait due to which spoofing of these simultaneously become more difficult for an imposter. In this paper, face and fingerprint are the two biometrics used to build multibiometric system. Here, fusion of two modalities is done at matching score level. An experimental result shows that the Multimodal biometric system is efficient compared to unimodal biometric system.
While multimodal biometric systems are considered to be more robust than unimodal ones but tradit... more While multimodal biometric systems are considered to be more robust than unimodal ones but traditional fusion rules are more sensitive to spoofing attempts. The proposed system is designed to overcome spoofing in worst-case scenario where impostor was able to create fake biometric traits of both face and fingerprint modalities in the presented multimodal biometric system. The paper investigates median filtering fusion rule as a spoofing resistant alternative to traditional sum rule based fusion rules. Experiments on the latest face video database (CASIA Face Anti-Spoofing Database) and fingerprint spoofing database (Fingerprint Liveness Detection Competition 2015) illustrate that the given system is more robust to spoofing attacks than the existing anti-spoofing methods even when m out of n samples of both the biometric traits to be combined are attacked. KeywordsMultimodal Biometrics, Anti-spoofing, Biometric feature extraction, Biometric score fusion.
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Papers by Sukhchain Kaur