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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 recomme...
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... calculate the expected value over the Softmax probabilities for age regression. Each step of the proposed approach shown in Figure 1 is depicted thoroughly in this section. In Figure 2, we present the process diagram of the smart store customer relationship management system based on our approach. ...
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... per the comparison table, the proposed method has a beneficial impact in estimating the age of a person over the same dataset and demonstrates better results than the traditional as well as deep learning-based age estimation models. The qualitative results of our model for the Morph dataset are presented in Figure 10. ...
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... stated in the calculation procedure of the cumulative score (CS), the CS values for the Morph dataset under distinct error thresholds are plotted in Figure 11. From the figure, a steady growth of the CS value is observed if the allowable error thresholds increase. ...
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... the figure, a steady growth of the CS value is observed if the allowable error thresholds increase. We plot the cross-entropy training and validation loss curve during the age estimation task in Figure 12. We stopped our training when the validation loss was increasing constantly. ...
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... assessed our model with multiple splits of the cross-validation protocol and present the mean value of the performance metrices belonging to gender classification in Table 7. In Figure 13, we present the best ROC (receiver operating characteristics) curves with corresponding area under the curve (AUC) scores for the gender estimation results. It is very hard to compare our system with other works fairly because the validation protocol and image settings are varying among the approaches. ...
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Citations
... There are plenty of motivations for gender recognition. For example, demographic factors like gender and age can be used by retailers to recommend products through customized advertising [3]. Besides that, it is also applicable on security: this type of information can be used to identify criminals and terrorists through surveillance cameras [4]. ...
Neural Architecture Search (NAS) is a busy research field growing exponentially in recent years. State-of-the-art deep neural networks usually require a specialist to fine-tune the model to solve a specific problem. NAS research aims to design neural network architectures automatically, thus easing the need for machine learning specialists to spend a lot of effort on hand-crafted attempts. As artificial intelligence applications are becoming ubiquitous, there is also a growing interest in efficient applications that could be deployed to smartphones, smart wearable devices, and other edge devices. Gender recognition in unfiltered images — such as those we find in real-world situations like pictures taken with smartphones and video shots from surveillance cameras — is one of such challenging applications. In this work, we developed an evolutionary NAS algorithm that consistently finds efficient ResNet-based architectures, named RENNAS, which have a good trade-off between classification accuracy and architectural and computational complexities. We demonstrate our algorithm's performance on Adience dataset of unfiltered images for gender recognition.
... All of the aforementioned CNN-based systems were evaluated based on the constrained dataset Morph for age estimation. In our recent paper [63], we demonstrated better MAE over the Morph dataset by pretraining a CNN network with the in-the-wild face image dataset, IMDB-WIKI. We ensured relative balancing among the 101 age classes that exist in the IMDB-WIKI dataset towards real age estimation. ...
... We only compare this with systems that estimate the biological age of a person rather than the apparent age or age group. We achieved better MAE in contrast to the works AGES [19], Kim et al. [63], OHRank [74], and CA-SVR [75], although our system handles face images with masks. In Table 5, we compare our system performance with the existing methods concerning the mean absolute error achieved irrespective of whether faces are masked or nonmasked. ...
... We only compare this with systems that estimate the biological age of a person rather than the apparent age or age group. We achieved better MAE in contrast to the works AGES [19], Kim et al. [63], OHRank [74], and CA-SVR [75], although our system handles face images with masks. In Table 5, we compare our system performance with the existing methods concerning the mean absolute error achieved irrespective of whether faces are masked or non-masked. ...
The lifestyle of humans has changed noticeably since the contagious COVID-19 disease struck globally. People should wear a face mask as a protective measure to curb the spread of the contagious disease. Consequently, real-world applications (i.e., electronic customer relationship management) dealing with human ages extracted from face images must migrate to a robust system proficient to estimate the age of a person wearing a face mask. In this paper, we proposed a hierarchical age estimation model from masked facial images in a group-to-specific manner rather than a single regression model because age progression across different age groups is quite dissimilar. Our intention was to squeeze the feature space among limited age classes so that the model could fairly discern age. We generated a synthetic masked face image dataset over the IMDB-WIKI face image dataset to train and validate our proposed model due to the absence of a benchmark masked face image dataset with real age annotations. We somewhat mitigated the data sparsity problem of the large public IMDB-WIKI dataset using off-the-shelf down-sampling and up-sampling techniques as required. The age estimation task was fully modeled like a deep classification problem, and expected ages were formulated from SoftMax probabilities. We performed a classification task by deploying multiple low-memory and higher-accuracy-based convolutional neural networks (CNNs). Our proposed hierarchical framework demonstrated marginal improvement in terms of mean absolute error (MAE) compared to the one-off model approach for masked face real age estimation. Moreover, this research is perhaps the maiden attempt to estimate the real age of a person from his/her masked face image.
