A Comparative Study of ResNet50 and YOLOv9 for Face Detection and Gender Classification
Aseil Nadhim Kadhim, Syahid Anuar, Saiful Adli Ismail · Engineering Technology & Applied Science Research · 2025
Gender classification based on facial features plays a central role in numerous intelligent applications such as surveillance cameras, interactive advertising, and human-computer interaction. This study aimed to compare two deep models, YOLOv9 and ResNet50, on face detection and gender classification, focusing on accuracy and inference speed. YOLOv9 performed well in terms of speed, with an inference time of 332 ms per image and a processing speed of 3 fps, and had a precision of 86.8%, 86.1% of recall, and 86.54% of F1-score. These performance characteristics make YOLOv9 suitable for real-time applications with high-speed response demands, even with moderately low classification accuracy. Conversely, ResNet50 was applied directly to gender classification after data preparation on images and had high classification accuracy, with a precision of 93.6%, 92% of recall, and 92.79% of F1-score. Its inference time was slower at 446.33 ms per image, with a 2.24 fps processing speed and a long training time of 9 hours and 18 minutes. These results show that YOLOv9 has high performance within a time scope of face detection, with reference to detecting enough faces within short timeframes with a limited number of computational resources, whereas ResNet50 has better classification accuracy. Depending on particular use case scenario demands, one corresponding model with a preferred feature can be selected: YOLOv9, if high-speed response is a concern during real-time applications, and ResNet50, if high classification accuracy is a concern.