A deep learning approach towards soft biometrics attributes prediction using CNN
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Any physical, behavioural or adhered human characteristics that we can observe from a person is known as Soft Biometric.The most common physical soft biometric attributes are height, age, ethnicity, facial hairs, gender, hair color etc. In this era of machine and deep learning, retrieving a person based on these semantic descriptions has become a major research interest. Face recognition and bounding boxes are now common implementations in IoT and surveillance systems because of the efficiency of training models. But the research on soft biometric attributes training models still lacks an amount. To overcome this, we have trained different CNN models for the best outcoming prediction result with a UTKface dataset. The dataset includes height, age and gender and 48x48 text pixels face images. The models include CNN, Multi-Headed CNN, DenseNet-169, Multi Label CNN and ResNet- 50. After training all the models we have found that the DenseNet-169 model can achieve the most accuracy for all the soft biometric classes in our dataset. The accuracy we have achieved with our model is 96.16% for age, 97.74% for ethnicity and 99.2% for age on our UTKface dataset keeping a training loss below 0.1 for the three soft-biometric traits. All the models have been trained into the same environment and it is being uploaded with the source code to the given link below: https://github.com/Kibria10/machine-learning-works.