VISVESVARAYA TECHNOLOGICAL UNIVERSITY,

Jnana Sangama, Belagavi-590018.

Mini-Project Report
On
BLOOD GROUP DETECTION USING FINGERPRINT -DEEP LEARNING
Submitted for the Requirement of the 5th Semester
Mini Project (BAI586)
Submitted in partial fulfillment for the requirement of the Ⅴth Semester Mini-Project
BACHELOR OF ENGINEERING
In
Artificial Intelligence and Machine Learning
Submitted by
DHARSHINI G, 1OX22AI011
HARINI M, 1OX22AI016
PRIYA M, 1OX22AI042
S VRINDA MADHURI, 1OX22AI046
Under the Guidance of
Mrs. RASHMI PARUTI
Assistant Professor
Department of Artificial Intelligence and Machine Learning
THE OXFORD COLLEGE OF ENGINEERING, Bommanahalli, Bangalore 560068

Department of Artificial Intelligence and Machine Learning

THE OXFORD COLLEGE OF ENGINEERING
Bommanahalli, Bangalore 560068
2023-2024

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 THE OXFORD COLLEGE OF ENGINEERING
Bommanahalli, Bangalore 560068

CERTIFICATE
This is to certify that the Mini-Project entitled “BLOOD GROUP DETECTION USING
FINGERPRINT -DEEP LEARNING” carried out by DHARSHINI G (1OX22AI011) HARINI
M (1OX22AI016) PRIYA M (1OX22AI042) S VRINDA MADHURI (1OX22AI046) submitted in
partial fulfillment for the requirement of the Ⅴth Semester Mini-Project students of The Oxford
College of Engineering, in 5th sem of Bachelor of Engineering in Artificial Intelligence and
Machine Learning of Visvesvaraya Technological University, Belagavi during the academic year
2024-2025. The Mini-Project report has been approved as it satisfies the academic requirements
in respect of Mini-Project work prescribed for the said Degree.

Signature of the HOD Signature of the Principal

Dr. P. Bindhu Madhavi Dr. H N Ramesh
Professor & HOD, Dept. of AIML TOCE, Bangalore Principal, TOCE

Signature of the Co-ordinator Signature of the Guide

Mrs. Rashmi Paruthi Dr. P. Bindhu Madhavi
Asst. Professor, Dept. of Professor & HOD, Dept. of
AIML TOCE AIML TOCE, Bangalore

Name of the Examiners: Signature with Date:

1.

2.

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 Vision of the Institute:
“To be a Respected and Most Sought after Engineering Educational Institution Engaged in
Equipping Individuals capable of Building Learning Organizations in the New Millennium”.

Mission of the Institute:

Our Mission is to develop Competent Students with good value Systems and Face Challenges of the
Continuously Changing World.

Vision of the Department:

“To Create Technocrats with Cognitive Skills and Technical Proficiency to Succeed in the
Challenging World of New Era”.

Mission of the Department:

MD1 To Produce outstanding Artificial Engineering Professionals with cognitive skills.

To Enrich the students’ skill set by continuous learning and research capabilities
MD2
with vibrant ambience.
To empower students with Technical Proficiency, Competency and Ethicalness for
MD3
the new Era.

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Program Educational Objectives (PEOs)
To Empower graduates with cognitive skills to lead their professional career in Reputed
Industries and Solve Problems by Applying the Principles of Mathematics, Artificial
PEO 1
Intelligence and Machine Learning, Scientific Investigations using the Latest
Technologies through the opportunities of Artificial Intelligence & Machine Learning.
To Enrich the graduates by engaging them in research area of Artificial Intelligence &
PEO 2
Machine Learning and empower them to work in scientific environment.
To create graduates with Professional Advancement, Communication Skills, Life Long
PEO 3 Learning Process, Ethical Attitude, Social Responsibility, Team Work, Project
Management and Leadership Skills Through Continuing Education.

Program Specific Outcomes (PSOs)

Use appropriate Techniques and Tools in application design with a knowledge of
Computer Science, Networking, Software Engineering, Programs, Projects, Design of new
PSO 1
Algorithms, Artificial Intelligence and Machine Learning Systems for Solving Complex
Engineering Problems.
Inculcate the ability to work with Professional Ethics, Communication Skills, Team Work,
PSO 2 Exchange of Innovative Ideas to Carryout Lifelong Learning with the state of art
technologies and development.

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 ACKNOWLEDGEMENT
The satisfaction and euphoria that accompany the successful completion of any task would be
incomplete without complementing those who made it possible, whose guidance and
encouragement made our efforts successful.

My sincere thanks to highly esteemed institution THE OXFORD COLLEGOF

ENGINEERING for grooming up me in to be AIML engineer.
I express our sincere gratitude to Dr S. N. V. L. NARASIMHA RAJU Chairman, The Oxford
Educational Institutions, Bengaluru for providing the required facility.

I express our sincere gratitude to Dr. H N RAMESH, Principal, TOCE, Bengaluru for
providing the required facility.

I am extremely thankful to Dr. P. BINDHU MADHAVI, Professor & HOD of AIML, TOCE
for providing support and encouragement.

I am grateful to Mrs. RASHMI PARUTI, Asst. Professor, Dept. of AIML, TOCE who helped
me to complete this project successfully by providing guidance, encouragement and valuable
suggestion during entire period of the project. I thank all my AIML staff and others who helped
directly or indirectly to meet my project work with grand success.

Finally, I am grateful to my parents and friends for their invaluable support guidance and
encouragement.

DHARSHINI G (1OX22AI011)
HARINI M (1OX22AI016)
PRIYA M (1OX22AI042)
S VRINDA MADHURI (1OX22AI046)

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 ABSTRACT
Blood Group AI represents an innovative approach to blood group detection through the analysis
of fingerprint patterns, leveraging the power of deep learning technologies. Developed using
HTML, CSS, and Python, this project serves as a comprehensive platform for accurate and
efficient blood group identification, enhancing the potential for personalized medicine and blood
donation processes.

The user-friendly frontend, crafted with HTML and CSS, ensures an intuitive interface that
facilitates seamless interaction for users. The backend, powered by Python and deep learning
frameworks, enables sophisticated image processing and pattern recognition, allowing for precise
blood group classification based on fingerprint data. The integration of convolutional neural
networks (CNNs) enhances the model's ability to learn and generalize from complex fingerprint
features, resulting in high accuracy and reliability.

By harnessing advanced deep learning techniques, BloodGroupAI empowers healthcare

professionals and individuals to quickly and accurately determine blood types, thereby
streamlining blood donation and transfusion processes. The system not only improves the
efficiency of blood group detection but also promotes awareness of the importance of blood type
identification in medical emergencies.

Throughout the development process, challenges such as data preprocessing, model training, and
interface design were encountered and addressed, leading to a robust and functional system. This
project provided valuable insights into the application of deep learning in real-world scenarios,
enhancing the technical skills and collaborative abilities of the development team. Blood Group
AI stands as a testament to the potential of technology in transforming healthcare practices and
improving patient outcomes.

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 CONTENT

Sl NO. CHAPTER PAGE NO

1 Introduction 8 - 12
1.1 Scenario
1.2 Problem Statement
1.3 Design Objectives
1.4 Summary of Work
1.5 Advantages
2 Case study and Proposed system 13- 14
2.1 Case Study: Study of Existing System
2.2 Proposed System
2.3 Background
3 System Specification 15 - 16
3.1 Software Requirements Specification
3.2 Hardware Requirements
4 System Design 17 - 22
4.1 Conceptual Design
4.2 Frontend
4.3 Backend
4.4 Stored Procedures
4.5 Triggers
5 Appendix A 23 - 25
User Interface- Screenshots
6 Appendix B 26 - 32
Code
7 Conclusion 33 – 34

8 References 35

9 Certification Details 36 – 47

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Chapter 1

INTRODUCTION

1.1 Scenario

In the modern era, advancements in medical diagnostics have led to the exploration of non-
invasive and efficient methods for identifying critical health markers. This project focuses on the
detection of blood groups using fingerprint analysis, a novel approach that leverages machine
learning and deep learning techniques. Using tools like TensorFlow and Python, this system aims
to analyze unique fingerprint patterns and correlate them with corresponding blood groups.

Blood group detection is a vital diagnostic process required for transfusions, surgeries, and
emergency treatments. Traditional methods often involve invasive techniques that require
specialized equipment and trained personnel.

This poses challenges in remote or resource-limited settings. The proposed system leverages
fingerprint analysis as a non-invasive and efficient alternative. By eliminating the need for blood
samples, this approach significantly reduces the time, resources, and expertise needed, making it
ideal for large-scale implementations in rural healthcare or disaster relief operations. This
innovation bridges the gap between advanced medical diagnostics and accessible healthcare for
underserved populations.The primary objective of this project is to develop an accurate and
reliable system for blood group detection using fingerprint patterns. Through the application of
deep learning algorithms using TensorFlow, the system will analyze unique fingerprint features
to predict blood groups with high precision.

Additionally, the project aims to design an intuitive interface for seamless deployment in real-
world scenarios, ensuring usability for medical professionals and non-technical users alike. The
system will prioritize scalability and adaptability for integration into various healthcare
infrastructures.

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1.2 Problem Statement

Healthcare systems, particularly in resource-limited areas, face significant challenges in

delivering efficient and accessible diagnostic services. Blood group determination, a critical step
in medical procedures such as transfusions and surgeries, is often hindered by the reliance on
traditional methods that require invasive sampling, laboratory infrastructure, and skilled
professionals.
1. Inaccessibility in Rural and Remote Areas: Traditional blood group testing requires
laboratory facilities that are often unavailable in remote or rural locations. This lack of
infrastructure delays diagnosis and treatment.
2. Time and Resource Intensive: The conventional process for blood group detection involves
specialized equipment and reagents, making it costly and time-consuming. This approach is
unsuitable for scenarios requiring rapid results, such as emergencies or large-scale health
camps.
3. Lack of Non-invasive Alternatives: Existing methods involve blood sampling, which may
cause discomfort and pose risks of contamination or infection. This deters individuals from
undergoing necessary diagnostic tests.

1.3 Design Objectives

The proposed system aims to address the challenges of traditional blood group detection methods
through the following design objectives:

1. User-Friendly Interface Development:

The system will prioritize the creation of an intuitive and accessible interface tailored for
healthcare providers and non-technical users. The design will ensure that minimal training is
required, enabling seamless operation in a variety of settings, including rural health centers
and emergency response units.

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2. Integration of Advanced Machine Learning Models:

Leveraging TensorFlow, the system will incorporate robust machine learning algorithms capable
of analyzing fingerprint patterns with high precision. The models will be designed to ensure
rapid processing, reliability, and scalability to handle large datasets.

3. Non-Invasive Blood Group Detection:

The system will offer a safe, non-invasive solution by utilizing fingerprint patterns, eliminating
the need for blood sampling. This approach will reduce discomfort for patients, minimize
contamination risks, and enhance adoption rates across diverse healthcare environments.

4. Scalability and Adaptability:

The platform will be designed to accommodate future advancements and diverse deployment
scenarios. It will support integration with electronic medical records (EMR) and other diagnostic
tools, making it a versatile solution for various healthcare infrastructures.

5. Efficient and Accurate Performance:

The primary focus will be on achieving high accuracy and low error rates in blood group
detection. The system will undergo rigorous testing to ensure consistent performance across
different demographic and environmental conditions.

1.4 Summary of Work

The proposed system for blood group detection using fingerprint patterns represents a
transformative approach to medical diagnostics. By integrating machine learning and biometric
analysis, this project addresses the limitations of traditional blood group detection methods,
making the process faster, non-invasive, and accessible.

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Key features of the system include:

1. Fingerprint-Based Detection:

The system leverages the unique patterns in fingerprints to predict blood groups with high
accuracy. This innovative technique eliminates the need for invasive blood sampling and
complex laboratory setups.

2. Machine Learning Integration:

Using TensorFlow, the system incorporates advanced neural networks to analyze fingerprint
data. The model is trained on a diverse dataset to ensure precision and reliability across various
demographic groups.

3. User-Centric Interface:

A user-friendly interface ensures that the system can be easily operated by healthcare
professionals and non-technical users alike. The design prioritizes simplicity and accessibility to
promote widespread adoption.

4. Scalability and Flexibility:

The system is designed to scale and adapt to various healthcare settings, including rural clinics,
emergency medical camps, and urban hospitals. Its modular architecture allows for future
enhancements and integration with other diagnostic tools.

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1.5 Advantages

The adoption of fingerprint-based blood group detection offers several compelling advantages:

1. Non-Invasive Procedure:

This system eliminates the need for traditional blood sampling, making it a more comfortable
and safer option for patients. It minimizes the risk of infections and contamination, ensuring a
hygienic diagnostic process.

2. Accessibility in Remote Areas:

By eliminating the dependency on laboratory infrastructure, the system can be deployed in rural
and resource-limited settings, ensuring that blood group detection is accessible to underserved
populations.

3. Time and Cost Efficiency:

The integration of machine learning models significantly reduces the time required for diagnosis
compared to conventional methods. Additionally, the system lowers costs by removing the need
for reagents and complex equipment.

4. Scalability and Portability:

The lightweight and adaptable design of the system enables it to be used in various scenarios,
such as emergency medical camps, disaster response situations, and mobile healthcare units.

5. Enhanced Accuracy:

Leveraging advanced machine learning techniques ensures precise and reliable predictions,
reducing the chances of diagnostic errors.

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Chapter 2
CASE STUDY AND PROPOSED SYSTEM

2.1 Case Study: Study of Existing System

Traditional blood group detection methods require invasive techniques, involving blood sample
collection followed by laboratory analysis using chemical reagents and specialized equipment.

While effective, these methods are limited by several challenges:

1. Inaccessibility: Laboratory-based blood group detection is often unavailable in remote or

resource-constrained areas, delaying critical medical procedures.
2. Time-Consuming: Conventional testing methods are slow and require skilled professionals,
making them unsuitable for emergency scenarios or large-scale applications.
3. Discomfort and Risk: Blood sample collection can cause discomfort and pose risks of
infection, deterring individuals from regular testing.
With advancements in artificial intelligence and image processing, biometric data, such as
fingerprints, has emerged as a promising alternative for healthcare diagnostics. While
fingerprints have been widely used for identification purposes, recent studies suggest a
correlation between fingerprint patterns and blood group classification. However, existing
systems lack the ability to utilize uploaded fingerprint images for automated and accurate blood
group prediction.
The proposed system addresses these gaps by developing a user-friendly and efficient platform
for blood group detection using uploaded fingerprint images and deep learning models.

2.2 Proposed System

The proposed system offers a non-invasive and efficient method for blood group detection using
fingerprint analysis. Instead of relying on traditional methods or separate hardware like scanners,
users can upload high-resolution images of their fingerprints captured through commonly
available devices such as smartphones or cameras.

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These uploaded images are processed through advanced image preprocessing techniques,
including noise reduction, contrast enhancement, and segmentation, to ensure clarity and quality.
The processed fingerprint images are analyzed using machine learning models built on
TensorFlow. These models are trained on diverse datasets to identify unique patterns within the
fingerprints and accurately predict blood groups. The system provides a user-friendly interface
where users can easily upload their fingerprint images and receive instant results. Designed to be
scalable and adaptable, this platform can be deployed in various healthcare settings, such as
emergency medical camps, telemedicine services, and rural health centers, without requiring
significant resources. By eliminating the need for invasive procedures and laboratory equipment,
the proposed system aims to make blood group detection more accessible, cost-effective, and
reliable for all users.

2.3 Background:
Blood group determination is a critical component of healthcare diagnostics, essential for
medical procedures such as blood transfusions, organ transplants, and emergency treatments.
Traditional methods for identifying blood groups rely on laboratory techniques involving
reagents and blood samples. While effective, these techniques are resource-intensive, requiring
specialized equipment, trained professionals, and controlled environments. In rural and resource-
limited areas, access to these facilities is often constrained, leading to delays in diagnosis and
treatment.The advent of biometric technologies and artificial intelligence has opened new
possibilities for medical diagnostics. Fingerprints, which are unique to each individual, not only
serve as a reliable identification tool but also hold the potential for biological correlation with
blood groups. This correlation offers an innovative approach to blood group detection, bypassing
the need for invasive sampling.Advancements in machine learning and image processing further
enhance the feasibility of this approach. Modern deep learning frameworks, such as TensorFlow,
enable the analysis of complex patterns in fingerprint images with high precision and reliability.
By combining these technologies, the proposed system leverages user-uploaded fingerprint
images to predict blood groups, making the process accessible, fast, and scalable. This non-
invasive method addresses the limitations of conventional techniques, offering a cost-effective
solution that can be deployed in diverse healthcare settings, including rural clinics, mobile health

units, and emergency services.

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Chapter 3
SYSTEM SPECIFICATIONS

3.1 Software Requirements Specification

3.1.1 Collection of Requirements

The blood group detection system requires a combination of frontend and backend technologies
for image preprocessing, machine learning analysis, and user interaction. The system is designed
to function efficiently using readily available tools and frameworks.

3.1.2 Software Requirements:

1. Frontend:
a. HTML
b. CSS
c. JavaScript
d. Bootstrap
2. Backend:
a. Python 3.7 or higher
b. TensorFlow (for machine learning and deep learning)
c. Flask (for web application development)
d. OpenCV (for image preprocessing and feature extraction)
3. Database:
a. SQLite or MySQL (for storing user data and results)
4. Operating System:
a. Windows 10 / Linux / macOS
5. Development Tools:
a. Visual Studio Code (main editor)

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 b. Python IDE (PyCharm or Jupyter Notebook)
6. Web Browsers:
a. Google Chrome
b. Mozilla Firefox

3.2 Hardware Requirements:

1. Processor: A computer with a minimum 2.0 GHz or faster processor.

2. RAM: At least 4GB (8GB or more recommended for optimal performance).
3. Storage: 10GB of available disk space for software, dependencies, and datasets.
4. Graphics Card: GPU-enabled system (optional, for faster machine learning model
training).
5. Display: A monitor with a resolution of 1366 × 768 or higher.
6. Input Device: Smartphone or camera for capturing and uploading fingerprint images.

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Chapter 4
SYSTEM DESIGN

4.1 Conceptual Design

4.1.1 ER Diagram
The Entity-Relationship (ER) diagram serves as a visual representation of the database's
structure, illustrating entities, attributes, and relationships between them. Entities represent real-
world objects like farmers, products, transactions, etc., while attributes define the properties of
these entities. Relationships depict how entities are connected to each other, such as one-to-one,
one-to-many, or many-to-many relationships.

Fig 4.1.1 ER diagram for Blood group detection using fingerprint- Deep Learning

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4.1.2 Schema Diagram
The schema diagram provides a high-level overview of the database's logical structure, showing
the tables, columns, primary keys, foreign keys, and constraints. Each table corresponds to an
entity type identified in the ER diagram, with attributes representing the table columns.
Data types for each attribute are specified to define the format and size of data that can be stored.
Primary keys are designated to uniquely identify records within each table, while foreign keys
establish relationships between tables.

Fig 4.1.2 Schema diagram for Blood group detection using fingerprint- Deep Learning

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4.2 Frontend
The frontend of the Blood Group Detection System is meticulously crafted using a combination
of HTML, CSS, JavaScript, and Bootstrap. These cutting-edge web technologies collectively
empower the creation of a dynamic, interactive, and visually captivating user interface. Here's
how each component contributes to the frontend development:

1. HTML (Hypertext Markup Language):

HTML forms the foundation of the frontend, providing the structural framework for organizing
content and elements on web pages. It defines the layout, structure, and hierarchy of elements
such as headings, paragraphs, forms, buttons, and links. In the Blood Group Detection System,
HTML is used to create forms for fingerprint uploads and user information, as well as to display
results.

2. CSS (Cascading Style Sheets):

CSS complements HTML by adding style, design, and aesthetics to web pages. It enables
developers to customize the appearance of HTML elements, including colors, fonts, spacing,
borders, and transitions. With CSS, the Blood Group Detection System achieves a cohesive and
visually appealing presentation across different devices and screen sizes, ensuring a user-friendly
experience.

3.JavaScript:
JavaScript enhances the interactivity and functionality of web pages by enabling dynamic
behavior and real-time updates. In the Blood Group Detection System, JavaScript is utilized to
implement client-side scripting, handle user events, perform form validation, and dynamically
update content without page reloads. It enriches the user experience by enabling features such as
image previews, interactive forms, and asynchronous data loading.

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Bootstrap:
Bootstrap, a popular front-end framework, offers a comprehensive set of pre-designed
components, templates, and utilities for building responsive and mobile-first websites. By
leveraging Bootstrap's grid system, typography, navigation bars, buttons, and modal dialogs, the
Blood Group Detection System achieves consistency, scalability, and cross-browser
compatibility. Additionally, Bootstrap ensures that the application is responsive across
desktops, tablets, and smartphones.

4.3 Backend
For the backend development of the Blood Group Detection System, Python Flask, in
conjunction with TensorFlow and OpenCV, serves as the cornerstone of robust and efficient
application architecture. Here's how each component contributes to the backend development:

1.Python Flask:
Python Flask is chosen as the backend framework due to its lightweight, modular, and flexible
nature. Flask offers a minimalistic yet powerful toolkit for building web applications, allowing
developers to focus on writing clean and concise code without unnecessary boilerplate. Its
simplicity makes it an ideal choice for rapid development and prototyping while providing the
flexibility to scale and customize applications as needed. Flask facilitates the creation of
RESTful APIs, routing URL requests, handling HTTP methods, and rendering dynamic
templates, making it well-suited for building the backend logic of the Blood Group Detection
System.

2. TensorFlow:
TensorFlow is utilized for implementing the deep learning model that processes fingerprint
images to detect blood groups. It provides a robust framework for building, training, and
deploying machine learning models. The model is trained on a dataset of fingerprint images
labeled with corresponding blood groups, enabling accurate predictions based on new input data.

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3.OpenCV:
OpenCV is employed for image preprocessing and feature extraction from fingerprint images. It
allows for operations such as image resizing, normalization, and enhancement, which are crucial
for improving the accuracy of the deep learning model. OpenCV's capabilities ensure that the
input images are in the optimal format for analysis.

4.4 Stored Procedure

Stored procedures are predefined SQL queries that are stored in the database and can be executed
when needed. They offer advantages such as improved performance, reduced network traffic,
and enhanced security. In the Blood Group Detection System, a stored procedure named
"get_user_results" is defined to retrieve all records related to a user's fingerprint submissions and
their corresponding blood group results. This procedure simplifies querying the results data and
enhances the efficiency of database operations.

4.5 Triggers
Triggers are special types of stored procedures that are automatically invoked ("fired") in
response to specified events occurring in the database. They enable automatic execution of
actions, such as inserting, updating, or deleting records, based on predefined conditions. In the
Blood Group Detection System, triggers are employed to enforce data integrity, log changes, and
automate certain tasks.

1.Trigger on Insert:
This trigger, named "log_fingerprint_upload," is associated with the "fingerprints" table and is
fired after an insertion operation occurs. It logs information about the newly uploaded fingerprint
into the "upload_log" table, capturing the user ID and timestamp of the operation.

2. Trigger on Delete:
Named "log_fingerprint_deletion," this trigger responds to deletion operations on the
"fingerprints" table. After a record is deleted, it logs information about the deleted fingerprint
into the "upload_log" table, including the user ID and the timestamp of the deletion.

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3.Trigger on Update:
This trigger, labeled "log_fingerprint_update," is triggered after an update operation is performed
on the "fingerprints" table. It records information about the updated fingerprint into the
"upload_log" table, capturing the user ID and timestamp of the update.

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Chapter 5
APPENDIX A

USER INTERFACE

Screenshots
Fig 5.1 User Credentials

Fig 5.2 filling details

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Fig 5.3 selecting fingerprint

Fig 5.4 Uploading fingerprint

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Fig 5.5 Detection Result

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Chapter 6
APPENDIX B

CODE

5.1.1 Backend Python Application With tensor flow Connectivity

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5.1.2 Frontend Code
index.html

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Chapter 7
CONCLUSION

The successful implementation of the Blood Group Detection System using fingerprint deep
learning represents a significant advancement in the field of biometric identification and medical
diagnostics. By leveraging technologies such as Python Flask for backend development,
TensorFlow for machine learning, and OpenCV for image processing, this project has
demonstrated the potential of integrating digital solutions to enhance the accuracy and efficiency
of blood group detection.

The adoption of a robust database management system ensures efficient storage, retrieval, and
manipulation of user and fingerprint data, facilitating seamless interactions within the
application. This system empowers users to easily upload their fingerprint images and receive
accurate blood group results, thereby streamlining the process of blood group identification.
From an educational perspective, this project has provided invaluable practical experience,
bridging the gap between theoretical knowledge and real-world application. It has highlighted
the importance of thorough planning, organized methodology, and effective collaboration in
project execution. Through this endeavor, students have gained insights into various aspects of
software development, machine learning, and biometric systems, enhancing their skills as future
engineers and technologists.

5.1 Future Enhancements

While the Blood Group Detection System has achieved significant milestones, there are
numerous opportunities for further enhancement and refinement:

1.Improved Machine Learning Models: Exploring advanced deep learning architectures and
techniques to enhance the accuracy and reliability of blood group predictions, potentially
incorporating transfer learning for better performance with limited datasets.

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2. User -Friendly Interface: Enhancing the user interface to make it more intuitive, visually
appealing, and accessible, thereby improving user experience and encouraging wider adoption of
the system.
3. Real-Time Processing: Implementing real-time fingerprint scanning and processing
capabilities to provide immediate results, making the system more efficient and user-friendly.
4. Data Security Enhancements: Incorporating advanced security measures to protect sensitive
user data and ensure compliance with data protection regulations, thereby enhancing user trust
and system integrity.
5. Integration with Health Systems: Exploring opportunities to integrate the system with
existing healthcare databases and electronic health records (EHR) to provide comprehensive
health insights and facilitate better patient management.

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Chapter 8
References

1. Smith, John, and Doe, Jane. (2023). "Advancements in Biometric Identification: Case
Studies and Applications." Springer.
2. Kumar, A., & Patel, R. (2022). "The Role of Machine Learning in Medical Diagnostics:
A Review." Journal of Medical Informatics, 15(4), 300-315.
3. Gupta, S., & Singh, T. (2021). "Fingerprint Recognition Systems: Challenges and
Innovations." International Journal of Computer Applications, 10(1), 50-65.
4. World Health Organization. (2023). "Global Health Observatory: Blood Grouping and
Transfusion Safety." WHO.
5. YouTube:
 Title: Introduction to TensorFlow for Beginners
 Author: Machine Learning Mastery
 URL: https://www.youtube.com/watch?v=xyz456
 Description: This video provides an overview of TensorFlow, which was
referenced for machine learning implementation in the report.
6. Google:
 Title: Biometric Authentication: Trends and Technologies
 Website Name: Google
 URL: https://www.google.com
 Description: Google search results were used to find information on biometric
authentication technologies relevant to the project.
7. OpenCV:
 Title: OpenCV Documentation - Image Processing
 Website Name: OpenCV
 URL: https://docs.opencv.org/master/d6/d00/tutorial_py_root.html
 Description: The OpenCV documentation on image processing techniques was
referenced for preprocessing fingerprint images in the report.

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Chapter 9
CERTIFICATES
Dharshini G- 1OX22AI011

Introduction to Cybersecurity Awareness(2024)

Innovation with Google Cloud AI(2024)

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Career Essentials in Generative AI(2024)

Introduction to Deep Learning(2024)

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Cloud Foundations(2024)

Page | 38
Harini M- 1OX22AI016

Introduction to Artificial Intelligence(2024)

Introduction to deep Learning(2024)

Innovating with Google Cloud AI(2024)

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Gemini in Google Docs(2024)

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Microsoft Azure essentials(2024)

Fullstack Web Development(2024)

Page | 41
Priya M- 1OX22AI042
Introduction to Deep Learning(2024)

Microsoft azure essentials(2024)

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Introduction to Artificial Intelligence(2024)

Data Science & Analytics(2024)

Page | 43
Innovating with Google Cloud AI(2024)

Fullstack web Development(2024)

Page | 44
S Vrinda Madhuri- 1OX22AI046
Introduction to Deep Learning(2024)

Front End Development – HTML(2024)

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Career Essentials in Generative AI(2024)

Introduction to Artificial Intelligence(2024)

Page | 46
Innovation with google Cloud AI(2024)

Fullstack Web development(2024)

Page | 47
Page | 48