Deep Learning based approach to detect Customer Age, Gender and Expression in Surveillance Video
This work addresses customer analytics for retail businesses by enabling demographic and expression detection from surveillance video, but it is incremental as it applies existing deep learning models to a specific domain.
The authors tackled the problem of predicting customer age, gender, and expression from surveillance video to enhance retail sales, achieving results evaluated on real-life garment store footage under challenging conditions like low resolution and occlusions.
In the current information era, customer analytics play a key role in the success of any business. Since customer demographics primarily dictate their preferences, identification and utilization of age & gender information of customers in sales forecasting, may maximize retail sales. In this work, we propose a computer vision based approach to age and gender prediction in surveillance video. The proposed approach leverage the effectiveness of Wide Residual Networks and Xception deep learning models to predict age and gender demographics of the consumers. The proposed approach is designed to work with raw video captured in a typical CCTV video surveillance system. The effectiveness of the proposed approach is evaluated on real-life garment store surveillance video, which is captured by low resolution camera, under non-uniform illumination, with occlusions due to crowding, and environmental noise. The system can also detect customer facial expressions during purchase in addition to demographics, that can be utilized to devise effective marketing strategies for their customer base, to maximize sales.