A Deep Dive into Gender Classification Using Inception V3: Performance and Insights
Poonam Shourie, Vatsala Anand, Rahul Singh Chauhan, Garima Verma, Sheifali Gupta · 2023
The study of images for the purpose of gender classification has garnered considerable interest in academic circles, owing to its wide-ranging applications in industries such as marketing, healthcare, security, and entertainment. This study investigates the utilization of the InceptionV3 convolutional neural network architecture in the context of gender classification. The InceptionV3 model, renowned for its profound and effective feature extraction skills, has been extensively employed in applications related to picture categorization. The aim of our study is to utilize the capabilities of InceptionV3 in order to effectively estimate gender labels based on facial photos. The utilization of the InceptionV3 architecture is thereafter employed to extract hierarchical features from the aforementioned photos. The unique inception modules included in the design incorporate convolutional filters of different sizes, which play a significant role in capturing multi-scale information that is essential for the task of gender classification. The findings demonstrate the effectiveness of InceptionV3 in properly predicting gender labels based on facial photos. The versatility of the architecture, its capacity to extract features, and its efficient utilization of GPU resources contribute to its robustness as a suitable option for gender categorization applications. However, it is important to highlight the necessity of ethical considerations, given that gender is a multifaceted and intricate construct that surpasses simplistic binary classification. This study makes a valuable contribution to the expanding field of research on gender classification and emphasizes the significance of deploying models in a responsible and unbiased manner.