Optimization of intelligent financial management system based on blockchain and internet of things

Here’s a breakdown of the provided text, focusing on the technical aspects of iris recognition and its integration with other systems:

Iris Feature Extraction (Gabor filters):

Gabor Filter: The text describes the use of a Gabor filter for iris feature extraction.This is a common technique in image processing, especially for texture analysis. Parameters of the Gabor Filter:
Phase: This parameter influences the phase of the filter’s response, which can affect the contrast of the output.
σ (Sigma): This is the standard deviation of the Gaussian envelope. It controls the size of the filter’s receptive field. Larger σ means a broader response too spatial frequencies.
γ (Gamma): This is the spatial aspect ratio. It determines the ellipticity of the receptive field. Smaller γ leads to a more circular shape, while larger γ results in a more elongated shape.
Feature Extraction: The filter is used to extract features like colour, texture, pupil boundary, and eyelash.
Feature Encoding: After extraction, the features are encoded.Quantization:

Purpose: The filtered images are encoded using a quantization method.This converts the continuous pixel values into a discrete binary code.
Method: A threshold value (T) is used.
If a pixel’s value in the filtered image ($I(x,y)$) is greater than or equal to the threshold (T), it’s represented as 1.
If the pixel’s value is less than the threshold (T), it’s represented as 0.
Equation (7):
$$
f(x,y) = begin{cases} 1 & text{if } I(x,y) ge T \ 0 & text{if } I(x,y) < T end{cases} $$ Outcome: This binary code is a compact and standardized representation of the iris features.

Role: The Daugman algorithm is mentioned as an efficient and accurate method for feature extraction in iris biometric verification. Process: It converts iris patterns into a digital template.

Approach: this is a popular method for matching iris biometrics.
Steps:
1. Acquisition: An iris image is captured.
2. Pre-processing: Noise and distortions are removed.
3. Feature Extraction: Relevant features (e.g., texture gradients, wavelet coefficients) are extracted to create a template.4. Comparison: The captured iris template is compared with a stored iris template using a matching algorithm.
5. Matching Score: A score is calculated to indicate the degree of similarity.
6. Decision: If the score exceeds a certain threshold, it’s considered a match.
Applications: This process is highly accurate and reliable for security systems and access control.Digital Template Storage:

Security: The digital template is stored in a secure, encrypted database, protected by passwords or other security measures.

Multi-Factor Authentication (MFA): The system uses three authentication factors. If all three match the enrolled user’s data, authentication is approved.
Blockchain: Used to establish the legitimacy of transactions and add them to the blockchain.
BMFA (Biometric Multi-Factor Authentication): This is implied as the system that combines iris biometrics with other factors.
Protection of Financial Data: Blockchain and BMFA techniques protect financial data from intermediate access.
proof-of-Work (PoW): Used as a consensus mechanism for transaction legitimacy on the blockchain.
RSA Encryption: Used to secure financial data during transmission and storage, ensuring privacy and security.

High Security: Offers a high level of security for financial resource management.
Transparency: Provides transparency in financial operations.
Fraud Reduction: Lowers the likelihood of fraud.

In essence,the text describes a sophisticated iris recognition system that leverages Gabor filters and quantization for feature extraction,Daugman’s algorithm for template creation,and template matching for verification. This biometric system is then integrated with blockchain technology and multi-factor authentication (BMFA) to enhance the security and transparency of financial transactions, employing PoW for consensus and RSA for data encryption.