Age detection by optimizing the structure of layers and neurons in the neural network

Age detection is a fundamental task in computer vision with numerous applications, from targeted advertising to security systems. This paper proposes a robust approach for age estimation based on local binary patterns to extract features associated with face images. The goal of accurately predicting people's ages from facial images is to overcome challenges such as changes in lighting conditions, poses, and facial expressions. The proposed method uses a combination of feature extraction, feature selection, and machine learning algorithms, which we named Hybrid method. At first, facial landmarks are detected to determine the key points of the face and enable the extraction of the corresponding facial features. These features are then fed into a feature selection algorithm to identify the most distinctive ones, reducing dimensionality and increasing model efficiency. To evaluate the proposed approach, extensive experiments are conducted on benchmark datasets, including different age groups and ethnicities. The results show the effectiveness of the proposed method in achieving high accuracy and robustness in age estimation. As shown in the calculation results, the detection rate and accuracy of Hybrid method age estimation calculations are better than competing methods. For Hybrid method, the mean absolute error is 4.94 years, with a standard deviation of 4.74 years. From the point of view of average absolute error, this age estimation method is superior to other methods that have been presented to date. The proposed method for estimating the age of people has a final sensitivity of 97.2%, an accuracy of 96.8%, and a precision of 99.1%. In addition, it is stated in the specifications of the implementation system that the program can be executed in about 3.5 s, which is a suitable speed for estimating the age of people based on their face photographs.