Enhancing Gender Classification in Higher Education: An Approach with Inception V3 and Backpropagation

Abstract

This research addresses the pressing need for efficient
and accurate gender classification of college applicants within
academic information systems. Current methods involve manual
gender selection, often leading to inaccuracies. We present a deep
learning model that automatically classifies gender using
passport-sized images, leveraging advanced techniques. Our
approach streamlines the admissions process, enhancing data
accuracy, and contributing to gender classification in educational
settings. In the realm of deep learning, we explore integrating
Inception V3 with Backpropagation, using features extracted
from Inception V3 for classification. We collected 160 balanced
training images and an unbiased validation dataset to ensure
real-world applicability. Results from our models (NN01-NN09)
demonstrate impressive accuracy, precision, and recall. NN02
consistently excels across all metrics, making it an ideal choice
for practical deployment. Validation results suggest room for
improvement in handling diverse data sources. In conclusion, our research improves gender classification in higher education,
emphasizing the value of modern technology. NN02 is
recommended for real-world applications, emphasizing the
significance of efficient gender classification in improving the
college applicant experience.