Effective Two-Stage Processing Based Lite Deep Learning Classifier for Gender Detection
Hua-Luen Chen, Chi-Chun Lai, Jie-Min Lin, Kuan-Hung Chen, Yin‐Tsung Hwang, Chih‐Peng Fan · 2021
For an intelligent autonomous mover, in addition to providing the functions of object detection and collision avoidance, gender classification has acquired more attentions recently due to its important role in user-friendly use in surrounding-crowds environments. In this study, we propose a two-stage processing based lite convolutional neural network (CNN) architecture for gender classification, where the pedestrians’ boxes inferred by the first stage detection are used as the inputs of the gender classification by the second stage process. By the proposed second-stage lite CNN-based model, the recognition accuracy of gender classification can be up to 99% with the image datasets collected in supermarket. Compared with the previous methodologies, the proposed two-stage processing approach performs higher recognition accuracy for gender classification.