Intelligent Biometric System using PCA and R-LDA

Shukla, Anupam; Dhar, Joydip; Prakash, Chandra; Sharma, Dhirender; R, Anand

doi:10.1109/gcis.2009.453

Cited by 10 publications

(5 citation statements)

References 13 publications

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“…The output ( y ) is linear combinations of inputs and can be computed, where i is index of input, l is index of neuron, and N is the number of input samples [ 28 ], as follows:

…”

Section: Methodsmentioning

confidence: 99%

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Online Handwritten Signature Verification Using Neural Network Classifier Based on Principal Component Analysis

Iranmanesh

Ahmad

Adnan

et al. 2014

The Scientific World Journal

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One of the main difficulties in designing online signature verification (OSV) system is to find the most distinctive features with high discriminating capabilities for the verification, particularly, with regard to the high variability which is inherent in genuine handwritten signatures, coupled with the possibility of skilled forgeries having close resemblance to the original counterparts. In this paper, we proposed a systematic approach to online signature verification through the use of multilayer perceptron (MLP) on a subset of principal component analysis (PCA) features. The proposed approach illustrates a feature selection technique on the usually discarded information from PCA computation, which can be significant in attaining reduced error rates. The experiment is performed using 4000 signature samples from SIGMA database, which yielded a false acceptance rate (FAR) of 7.4% and a false rejection rate (FRR) of 6.4%.

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“…The output ( y ) is linear combinations of inputs and can be computed, where i is index of input, l is index of neuron, and N is the number of input samples [ 28 ], as follows:

…”

Section: Methodsmentioning

confidence: 99%

“…Then, the output ( y ) is compared with the desired output, resulting in an error ( e ). The following equation shows how error is calculated, where tl are the target values and ol are the output values [ 28 ]:

…”

Section: Methodsmentioning

confidence: 99%

Online Handwritten Signature Verification Using Neural Network Classifier Based on Principal Component Analysis

Iranmanesh

Ahmad

Adnan

et al. 2014

The Scientific World Journal

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“…The model uses the commercial face recognition system FaceIt for the task of facial recognition. This used Local Feature Analysis (LFA) for the facial recognition (Penev and Atick 1996;Penev 1998Penev , 1999Shukla et al 2009aShukla et al , 2009b. We know that the face is an interconnection of various interesting parts like eyes, lips, nose, etc.…”

Section: Facementioning

confidence: 99%

Multimodal Biometric Systems

Shukla¹,

Tiwari²,

Kala³

2010

Towards Hybrid and Adaptive Computing

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Abstract. The uni-modal biometric systems making use of a single biometric modality have a limited performance that restricts their applicability in real life scenarios. The multimodal biometric systems make use of two or more modalities that together achieve much higher performances. In this chapter we discuss the means to fuse three modalities to make a more robust system. We first discuss the fusion of speech, lip, and face. This system uses Hidden Markov Models for the classification and an integration technique called as late integration for decision making from the three modalities. We then discuss the fusion of face, speech and fingerprint. Here each of the individual biometric modalities would make use of modular neural network which would then be combined using a fuzzy integration technique. The last model we discuss would carry the fusion of fingerprint, face and hand geometry. This system uses a variety of fusion techniques including a sum rule, linear discriminant function and decision trees.

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“…The basic approach is to compute the eigenvectors of the covariance matrix of the original data, and to approximate it by making a linear combination of the leading eigenvectors [17]. By using the PCA procedure, the test vector can be identified by first, projecting the image onto the eigenvector space to obtain the corresponding set of weights, and then by comparing it with the set of weights for the vectors in the training set [18,19].…”

Section: Principal Component Analysismentioning

confidence: 99%

Multimodal Biometric Using a Hierarchical Fusion of a Person's Face, Voice, and Online Signature

Elmir¹,

Elberrichi²,

Adjoudj³

2014

Journal of Information Processing Systems

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Abstract-Biometric performance improvement is a challenging task. In this paper, a hierarchical strategy fusion based on multimodal biometric system is presented. This strategy relies on a combination of several biometric traits using a multi-level biometric fusion hierarchy. The multi-level biometric fusion includes a pre-classification fusion with optimal feature selection and a post-classification fusion that is based on the similarity of the maximum of matching scores. The proposed solution enhances biometric recognition performances based on suitable feature selection and reduction, such as principal component analysis (PCA) and linear discriminant analysis (LDA), as much as not all of the feature vectors components support the performance improvement degree.

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Intelligent Biometric System using PCA and R-LDA

Cited by 10 publications

References 13 publications

Online Handwritten Signature Verification Using Neural Network Classifier Based on Principal Component Analysis

Online Handwritten Signature Verification Using Neural Network Classifier Based on Principal Component Analysis

Multimodal Biometric Systems

Multimodal Biometric Using a Hierarchical Fusion of a Person's Face, Voice, and Online Signature

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