A HYBRID APPROACH FOR GENERATING BINARY SECURED FACE TEMPLATE (BDA)

Dhananjay Dewangan

Hybrid Approach

• Combination of both methods, Biometric cryptosystem and cancellable biometrics.

Binary Discriminant Analysis

• The rationale of the proposed method is to use a set of n linear discriminant function to transform a real-valued template into a n dimensional binary face template.

Binary Discriminant Analysis

• The key issue is how to find the optimal linear discriminant functions so that the discriminability of the binary templates is maximized.

• To maximize the discriminability Between class variance should be maximum and Within class variance should be minimum.

• To minimize within class variance, we use perceptron learning rule

• To maximize between class variance we make use of BCH codes.

Perceptron Learning Rule• The perceptron is employed to find optimal linear discriminant

function (LDFs) so that the output binary templates are close to the corresponding target binary template. Thus, the within-class variance of the binary templates is minimized.

• If two classes C1 and C0 are linearly separable, a linear discriminant functiong(x) = wTx + tis constructed so that for a random sample in these two classes,g(x) > 0, if x Ɛ C1g(x) ≤ 0, if x Ɛ C0

Proposed Method - Enrolment

BCH Codes(255,37,45)

Feature Extraction

(DCT)

Perceptron Learning Rule

Compare

SHA-256

Database (Stores Hashed Password and Weight & Bias matrix)

Weight & Bias

Password (5 digit)Alphanumeric

Biometric Template( Face)

Identical

Not Identical

Proposed Method - Authentication

Feature Extraction

• In our project we are using DCT transform for feature extraction.

Feature Extraction• DCT Coefficient Scanning Order

Currently we are using first approx 500 coefficients in zig zag order but it can be reduced to less number by taking large no of sample of same facial image varying its Illumination, Pose, Expression and Lighting conditions.

• BCH CodesIn our project, we have used BCH [255,37,45] code.

• Hashing Algorithm We have used SHA 256 hash algorithm to secure the confidential data(password).

• Pre-processingWe have taken 256 bit length BCH code and SHA 256 takes input length 512 bit hence we need to preprocess it. Append the bit '1' to the message .Append k bits '0', where k is the minimum number >= 0 such that the resulting message length (modulo 512 in bits) is 448.Append length of message (without the '1' bit or padding), in bits, as 64-bit big-endian integer (this will make the entire post-processed length a multiple of 512 bits)

Performance Analysis

Image Database

Image Database contains two types of database• Password – Secured Hash• Weight & Bias matrix – Adjusted in binary

format.

Image Database (cont)

Experimental Analysis

• Between Class VarianceThe BCH coding scheme preserves the minimum hamming distance between its each code. For BCH (255, 37, 45), the minimum hamming distance is (2x45+1)=91 and the corresponding variance is (91)^2/4 which can be considered as strong between class variance and also helps to improve performance of our biometric cryptosystems.

Experimental Analysis

• Within Class Variance In our project we have taken more number of images of each individual having different pose and expression. As the no of image increases, the correlation between them also increases which leads to less variance. . Another importance of having more images of each individual is decrease in storage requirements since only few DCT coefficients or low frequency components need to be retained.

Experimental Analysis

• RevocabilityIn our project we have adopted the BCH coding scheme as a target which is further stored in the database after employing hashing technique such as SHA-256 for security purpose. This coding scheme is applied over the user’s password. So if the password is changed in case of templates compromisation, its BCH code will be changed and their corresponding hash will also be changed which leads to a different data in the database to be stored

Experimental Analysis

• Performance In our project, database contains not directly

images but secured binary representation of such images. So we can conclude that the binarization and their hash don’t affect the actual performance of our biometric cryptosystem.

Experimental Analysis

• SecurityIn our project, we have used SHA-256 to

accommodate the security of face template which provides the security level over birthday attack of 2^128 which is quite enough for any moderate level application.

Experimental Analysis

• Diversity Since SHA algorithm is noninvertible and the weight and bias matrix doesn’t reveal any information, the imposter has to make an attempt for both what the password is and what the relevant face should look like. Therefore the brute-force attack is nearly impossible for 5 digit password (94^5) consisting 94 different alphanumeric key. Since it is a two stage verification systems, it provides enough security and strong diversity for moderate level application.

Future Scope

• In order to achieve higher level of security, more number of biometric traits are required to authenticate whether the user is genuine or imposter. So multimodal biometric cryptosystem can be implemented by the same proposed method along with other biometric traits.

• For higher level of security ,BCH coding schemes and corresponding hashing techniques can be changed to more number of bits as per application requirements and number of users

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