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.