A Smart Prediction Model for Age and Gender from Facial Images Using CNN

The identification of age and gender from facial images is also difficult because of the variations in lighting condition, different facial poses, the presence of occlusions, as well as the problem of imbalanced gender data set. Consequences of inaccurate prediction include the following: This could be observed in surveillance systems resulting in the failure of offering a productive and effective individual marketing the result of these inaccuracies could also be seen in social and demographic studies where unsuitable results could be generated. To overcome these difficulties, this paper utilizes a novel CNN architecture that is designed specifically for the task. The CNN model design includes convolutional and max-pooling layers and fully connected layers with dropout to reduce the overfitting problem. This means that the accuracy and reliability of age and gender prediction is improved by the use of this approach, making it suitable for security services, marketing realms and human-computer interface systems. There exists a reasonable improvement in the model when benchmark datasets are applied; thus, the custom CNN model is a reliable tool for practical tasks.