It is a presentation about building a machine-learning model that classifies blood groups of humans using their fingerprints.

1. Team Members:
C Harshini. -21r11A66A4
Dn Sathwika -21R11A66A9
S Pranay Kumar-21R11A66D9

2. TABLE OF CONTENTS
Abstract
Objectives
Project Scope
Concepts used in project
Identification of problems
Time Plan of project
Conclusion
02

3. ABSTRACT
This project explores the feasibility of using deep learning to detect blood
groups from fingerprint images.
Traditional blood group detection methods require invasive blood sample
collection and laboratory analysis, which can be time- consuming and
resource-intensive.
In contrast, this approach aims to provide a non-invasive, rapid, and
accessible alternative.
By leveraging a Convolutional Neural Network (CNN) architecture, we analyze
fingerprint patterns to predict blood groups (A, B, AB, O). A dataset of
fingerprint images with corresponding blood group labels is collected and
preprocessed, including normalization and augmentation techniques

4. OBJECTIVES
Develop a Deep Learning Model: Create a model that predicts blood group based
on fingerprint images using architectures like CNNs.
Fingerprint Feature Extraction: Identify relevant features from fingerprint patterns
(e.g., minutiae points, ridges) for blood group prediction.
Address Bias and Fairness: Ensure the model performs fairly across various
demographic groups (age, gender, ethnicity).
Ethical and Privacy Concerns: Ensure the privacy, security, and ethical use of
fingerprint data, following relevant guidelines.
Generalize Across Populations: Ensure the model generalizes well across different
populations and regions.

5. PROJECT SCOPE
Real-Time Blood Group Prediction: Instant blood group identification from
fingerprint scans.
Integration with Healthcare Systems: Seamless integration with hospital or
clinic management systems for quick access.
Emergency Use: Fast blood group identification for critical medical decisions in
emergencies.
Low Latency and High Accuracy: Ensure fast processing and accurate results in
real-time.

6. CONCEPTS USED IN THIS
PROJECT
K-Nearest Neighbors (KNN): KNN classifies fingerprint
images by comparing a test image’s features to the
nearest labeled samples, categorizing blood groups
based on similarity to known data.
CNN (Convolutional Neural Networks): Extracts
hierarchical patterns from fingerprint images,
automatically detecting key features like ridges and
minutiae, and then classifying the image to predict
blood groups.

7. CONCEPTS USED IN THIS
PROJECT
SVM (Support Vector Machine): After feature
extraction, SVM is used for classifying the blood group
by finding optimal hyperplanes to separate different
classes of blood groups based on the features
identified by the CNN.
Data Augmentation: Applied to artificially expand the
training dataset by applying transformations like
rotations and scaling, which helps improve the
model's robustness and generalization.

8. IDENTIFICATION OF PROBELM
Accuracy limitations: The accuracy of fingerprint-based
blood group prediction might not be as high as
traditional blood tests. This could lead to
misidentification of blood types, which can have serious
consequences in medical situations.
Privacy concerns: Fingerprint data is sensitive biometric
information. There are concerns about the security and
privacy of this data, especially if it is stored or
transmitted electronically.
Technical challenges: Developing a reliable and
accurate fingerprint-based blood group prediction
system is a complex technical challenge.

9. 09
TIME PLAN OF
PROJECT

11. THANK YOU