Multimodal Biometric Security Presented By: Qurratulain PhD Scholar SEIEE Department, Shanghai Jiao Tong University Email:qurratulain291@gmail.com

Introduction An application of modern statistical methods to the measurements of biological objects PIN Identification of an individual on the basis of “Who you are?” rather than “What do you know?” Knowledge-based security, e.g. a password , a PIN.. “What do you have?” Token-based security, e.g. passport, driver’s license, ID card.. Know Be Have

Introduction Working of a biometric system Test Test

An example of multibiometric system Introduction Sensors Extractors Image- and signal- pro. algo. Classifiers Negotiator Threshold Biometrics Voice, signature acoustics, face, fingerprint, iris, hand geometry, etc Data Rep. 1D (wav), 2D (bmp, tiff, png) Feature Vectors Scores Enrolment Training Submission An example of multibiometric system

Introduction Need for Multimodal Biometrics Limitations Increase security Make it possible, automatically, to know WHO did WHAT, WHERE and WHEN! Replace hard-to-remember passwords Prevent unauthorized use of lost, stolen or "borrowed" ID cards Limitations Noise in sensed data results in a significant reduction in accuracy For voice and signature, circumvention by spoofed attacks

Problem Statement The use of signature verification is a time-consuming process for the user in real time situations User-involvement at image acquisition stage reduces the time efficiency of the identification process and causes delay Mostly PCA and LDA based global features are used for feature extraction and superiority of LDA over PCA has been affirmed many times so far, therefore a newer approach for feature extraction is required that can outperform the existing methods e.g. Discrete Cosine Transform (DCT) because of its energy compaction property

Problem Statement Moreover, security concerns have been increased because of the following drawbacks of unimodal biometric systems: Noise in sensed data results in a significant reduction in accuracy Non-universality leads to Failure To Enroll error (FTE) Lack of Individuality or Uniqueness increases False Accept Rate (FAR) Openness to circumvention by spoofed attacks commonly for voice and signature

Solution A multibiometric system can be used based on feature Level fusion and feature vector optimization through genetic algorithm. Multibiometric system can be implemented so that it can reduce error rate caused by unibiometric systems, e.g., If the biometric sample obtained from one of the sources is not of sufficient quality during a particular acquisition, the samples from other sources may still provide sufficient discriminatory information to enable reliable decision-making. Non-Universality If a person cannot be enrolled in a finger print system due to worn-out ridge details, he can still be identified using other biometric traits like face or iris Noisy data

Solution Discrete Cosine Transform (DCT) can be employed along with the old techniques of feature extraction like PCA and LDA DCT has been selected as it offers many benefits: Provides good compromise between energy packing ability and computational complexity The energy packing property of DCT is superior to that of any other unitary transform Transforms that redistribute or pack the most information into the fewest coefficients Provides the best sub-image approximations and, consequently, the smallest reconstruction errors

Solution A multimodal biometric system can be introduced that reduces user involvement at the image acquisition stage Discrete Cosine Transform (DCT) can be employed along with the old techniques of feature extraction like PCA and LDA Normalization of feature vector can be performed using z-score Normalized feature vectors can be fused to form multibiometric system, fusion being done by product, sum and min rules Genetic Algorithm (GA) can be used for feature vector optimization after feature level fusion Selected features can then be incorporated into a Bayesian Classifier for calculating the accuracy of the proposed system

Proposed Solution Face image Iris image Ear image Face Preprocessing Iris Preprocessing Ear Preprocessing Face, Iris, and Ear Features DCT System Face, Iris, and Ear Features PCA System Face, Iris, and Ear Features LDA System Feature Vector Normalization Feature Vector Normalization Feature Vector Normalization Feature level Fusion Optimization using Genetic Algorithm Bayesian Classifier

Future Work In the field of Multibiometrics, there is still a lot of work requied to reduce the effects of noisy data and to increase template security The work presented in this thesis can be extended: By implementing different variation of PCA and LDA like Multilinear PCA, kernel PCA, Independent Discriminant Analysis, etc By using the energy compaction property of DCT on a feature extraction method of LDA, and PCA. In this way the feature vector of LDA and PCA systems would be optimized by DCT By employing various fusion techniques in parallel and concatenating the resultant vectors. The different levels of fusion can be evaluated for making a comparison on the multibiometric system

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