• Manoj kumar Naidu Latchubugatha
• John Jaidhan B

Researchers have shown more interest in the soft biometrics area to fill the communication gaps between humans and machines with the growth of real-world application has increased day to day life. Soft-biometric consists of age, gender, ethnicity, height, facial measurements and etc. This paper contains a detailed discussion about the contribution of the researchers in the area of gender classification and age estimation using neural networking. Most of the work is done using Convolutional neural networks and auto encoders. Various elements related to neural network models such as dataset, findings, calculative metrics and results are embraced for effortless interpretation of tabular correlation research. Finally, the authors summarize germane tasks for future various research aspects. We plan a primary study on increasing the performance of pre-trained deep networks by applying post-processing strategies. The main advantage with respect to fine-tuning strategies consists of the simplicity and low computational cost of the post-processing step. To the best of depart our knowledge, this paper is the first study on age estimation that proposes these of post-processing strategies for features extracted using pre-trained deep networks. Our method exploits a set of pre-trained Convolutional Neural Networks (CNNs) to extract features from the input face image. The method then performs a feature level fusion, reduces the dimensional of the feature space, and estimates the age of the individual by using a Feed-Forward Neural Network (FFNN). We evaluated the performance of our method on a public dataset (Audience Benchmark of Unfiltered Faces for Gender and Age Classification) and on a dataset of non-ideal samples affected by controlled rotations, which we collected in our laboratory. Our age estimation method obtained better or comparable results with respect to state-of-the-art techniques and achieved satisfactory performance in non-ideal conditions. Results also showed that CNNs trained on general datasets can obtain satisfactory accuracy for different types of validation images, also without applying fine-tuning methods

Soft Biometrics, Neural Nets, CNN, Gender recognition, Age estimation

"A Study on a Structure Based Human Facial Age Estimation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 6, page no.b781-b786, June-2021, Available :http://www.jetir.org/papers/JETIR2106251.pdf

"A Study on a Structure Based Human Facial Age Estimation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 6, page no. ppb781-b786, June-2021, Available at : http://www.jetir.org/papers/JETIR2106251.pdf