###### Abstract  
The COVID-19 pandemic markedly changed the human shopping nature, necessitating a contactless shopping system to curb the spread of the contagious disease efficiently. Consequently, a customer opts for a store where it is possible to avoid physical contacts and shorten the shopping process with extended services such as personalized product recommendations. Automatic age and gender estimation of a customer in a smart store strongly benefit the consumer by providing personalized advertisement and product recommendation; similarly, it aids the smart store proprietor to promote sales and develop an inventory perpetually for the future retail. In our paper, we propose a deep learning-founded enterprise solution for smart store customer relationship management (CRM), which allows us to predict the age and gender from a customer's face image taken in an unconstrained environment to facilitate the smart store's extended services, as it is expected for a modern venture. For the age estimation problem, we mitigate the data sparsity problem of the large public IMDB-WIKI dataset by image enhancement from another dataset and perform data augmentation as required. We handle our classification tasks utilizing an empirically leading pre-trained convolutional neural network (CNN), the VGG-16 network, and incorporate batch normalization. Especially, the age estimation task is posed as a deep classification problem followed by a multinomial logistic regression first-moment refinement. We validate our system for two standard benchmarks, one for each task, and demonstrate state-of-the-art performance for both real age and gender estimation. deep learning; age estimation; gender estimation; smart store; COVID-19 +
Footnote †: 2021: 11, 4549. [[https://doi.org/10.3390/app11104549](https://doi.org/10.3390/app11104549)]([https://doi.org/10.3390/app11104549](https://doi.org/10.3390/app11104549))
Owing to the uprising trends of intelligent systems, there is an increased demand for automatic human demographic extraction from face images. The estimation of human demographics such as age and gender from face images is a very promising and challenging task in academia and industry. Application where age and gender estimation can play a useful part include (i) access control [1], e.g., curbing the entry of an underaged person to sensible items from vending machines or to an event where only people of specific gender can join; (ii) human-computer interaction (HCI) [2; 3], e.g., providing a different product advertisement or offer by looking at the gender and age of a person automatically; (iii) law enforcement [4], e.g., a criminal demographic estimation can help the law enforcement agency to find out the suspects more proficiently from previous records; (iv) surveillance [5], e.g., an automated system recognizing unattended minors to some unexpected places and times; (v) electronic customer relationship management [6], e.g., companies may use internet-based platforms to interact with customers to perceive their preferences and customize their store products. Accordingly, many traditional retail systems migrate to the intelligent system with the recent development of technology such as smart store retail management. To ease the shopping process and the future retail of smart stores, the age and gender estimation of a customer is indispensable. Similarly, for welcoming salutations, the content and manner are quite different based on age and gender. In order to achieve these tasks, major challenges arise due to the unconstrained real-world face images which are captured in a different angle, pose, and background. A significant amount of research has been conducted to estimate the age from a face image having the form of real or biological age estimation. This age and gender estimation research spans decades, as summarized in large studies [7; 8; 9; 10; 11]. Each of these face analysis tasks (age and gender estimation) are sought to solve distinct research problems through a variety of techniques [12; 13; 14; 15; 16; 17].