Information fusion in biometrics

By Arun A. Ross, Anil K. Jain, J. Qian. Pattern Recognition Letters, 2003

1,919 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/1c1c4182c94e307d603443d6b5f966e266403672

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Citations in the corpus

SDUMLA-HMT: A Multimodal Biometric Database (2011, background): “Therefore, many researchers recently put their efforts to multimodal biometric fusion [1][2][3].”
Wireless device identification with radiometric signatures (2008, background): “[35], while a biometric perspective can be found in [33, 36].”
Matching Forensic Sketches to Mug Shot Photos (2011, methodology): “fusion [29] of the match scores from SIFT matching and MLBP”
Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems (2005, neutral): “Ross and Jain [9] combined face, fingerprint, and hand geometry biometrics with sum, decision tree, and linear discriminant-based methods.”
AccelPrint: Imperfections of Accelerometers Make Smartphones Trackable (2014, background): “Fingerprints are originally used as a biometrics technology to identify human beings [58], [62].”
Multimodal fusion vulnerability to non-zero effort (spoof) imposters (2010, background): “Multi-biometric systems have commonly taken three forms; single biometric trait multiple representation, single biometric trait multiple matcher, and multiple biometric trait [3].”
Anti-spoofing in Action: Joint Operation with a Verification System (2013, background): “Examples of these learning-based fusion rules are k-means or fuzzy clustering [6], Support Vector Machines (SVM) [4], [11], Linear Discriminant Analysis (LDA) [4], [23], [26], decision trees [4], [23], multi-layer perceptron [4], [26], Behavior Knowledge Space [22], [25] and many more.”
Fusing Gabor and LBP Feature Sets for Kernel-Based Face Recognition (2007, background): “There is some work on fusing different biometric modalities ( e.g . face and speech [3], face and ﬁngerprint [23]), but most studies concentrate on fusing different representations of a single underlying modality (e.g. 2D and 3D facial shape in [6]).”
Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing (2008, methodology): “Further, the performance of the proposed 2ν-SVM fusion algorithm is compared with Sum rule [33], [34], Min/Max rule [33], [34], and kernel based fusion rule [35] .”
A survey of palmprint recognition (2009, background): “Fusion is a promising approach that may increase the accuracy of systems [77].”
Personal Verification Using Palmprint and Hand Geometry Biometric (2003, background): “The multiple pieces of evidences can be combined by a number of information fusion strategies that have been proposed in the literature [29]-[31].”
Feature level fusion of hand and face biometrics (2005, methodology): “Multibiometric systems seek to alleviate some of the drawbacks encountered by unibiometric systems by consolidating the evidence presented by multiple biometric traits/sources.(2) These systems can significantly improve the recognition performance of a biometric system besides improving population c”
HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users (2015, positive): “We chose this method because it is simple to implement, and has been shown to perform well in biometrics [30].”
Biometrics: a grand challenge (2004, background): “One would also like to understand how to seamlessly integrate multiple biometric cues [20] to”
Fusing Multiple Features for Depth-Based Action Recognition (2015, methodology): “Data fusion has been studied extensively, and shown great performance in many areas, 2010], human identiﬁcation [Ben-Yacoub et al. 1999], face recognition [Chang et al. 2003], handwriting recognition [Xu et al. 1992], biometrics [Ross and Jain 2003], and so forth.”
Score normalization in multimodal biometric systems (2005, methodology): “Jain and Ross [22] have proposed the use of user-specific weights for computing the weighted sum of scores from the different modalities.”