###### Abstract
The facial attribute information such as age and gender are already being predicted using facial landmark information [15; 16; 17; 18; 19; 20; 21]. Spotting accurate facial landmarks is in itself a challenging issue. Localization of the facial points is heavily complicated in some imaging conditions, e.g., when the face is occluded by something, rotated extremely, complex facial expressions, and the resolution of the image is very low. Analogously, landmark extraction from faces is practically impossible when the imaging environment is in the far-field. It is worth bearing in mind that the real-world smart applications for age and gender estimation need to tackle faces having certain unconstrained environments, like improperly aligned or having unusual pose and expressions. Therefore, under these circumstances, prior to input a face to the age and gender estimation system, a face should be detected first and, in a next step, properly aligned. Despite the recent progress made in [7; 22; 23] in the context of handling faces in the wild, the accurate prediction of age and gender remains a challenging problem due to the limited and constrained image datasets. Hence, a shallow network was proposed to classify the age and gender in [23]. In [24], authors utilized the benefit of a manageable deep neural network to train with a large and diverse face image dataset. A robust face detection and alignment operation was performed over the in-the-wild face images that play a noticeable role in the overall performance. Although this work achieves very good performance for the real age estimation task, this network is biased to the classes that belong to adult people because of the large inter-class sample variation problem that exists in the training dataset. In this paper, we approach an integrated framework for human age and gender estimation being motivated by the contemporary deep learning-based advancement in the associated research on age and gender estimation [23; 24]. We demonstrate better results for age estimation by a substantial margin compared to the state-of-the-art (SOA) approach [24] with necessary improvement in the training data and imposing more regularization techniques (i.e., batch normalization, data augmentation) in the network. Subsequently, we achieve better results in anticipation of gender classification compared to the SOA method [23]. The contribution of this work is summarized as follows:We propose an automatic age and gender estimation system for the customers of a smart store (e.g., Amazon Go, SmartMart) to affluence the offline smart shopping and to update the future stock by analyzing the customer demographics (i.e., age and gender) due to the new shopping nature amid the Covid-19 pandemic situation. * We handle the data sparsity problem (see Section 3.1.1) that exists in the publicly available in-the-wild face image dataset IMDB-WIKI [24]. * We consider both age and gender estimation tasks as a classification problem and deploy the ImageNet pre-trained model VGG-16 [25], although real age estimation is basically a regression problem. We address the age estimation problem like [24], with an effective change in the dataset by making it almost balanced and introduce a batch normalization layer to speed up the learning process along with stable performance because a smart store needs a robust system. * For the comparable results, we evaluate our model on the constrained and specific aged people image dataset Morph [1]. Subsequently, the challenging Adience [9] image dataset is used to evaluate the gender estimation performance. Our approach marginally outperforms the state-of-the-art methods in both age and gender estimation tasks. The anatomy of the rest of the paper is as follows: Section 2 elucidates the literature regarding age and gender estimation. Section 3 presents our proposed method. The insight of the experiments and attained results are presented in Section 4. Section 5 carries out the comparative discussion regarding age and gender estimation results, and Section 6 concludes this research.