Age Range Estimation using MTCNN and VGG-Face Model

TL;DR

This paper presents an age range estimation method using MTCNN for face detection and VGG-Face with transfer learning, achieving superior accuracy on the Adience Benchmark.

Contribution

It introduces a novel combination of face detection and transfer learning with VGG-Face for improved age range estimation accuracy.

Findings

Outperformed existing state-of-the-art methods on the Adience Benchmark.

Used data augmentation with random cropping to enhance model performance.

Validated the effectiveness of transfer learning in age estimation tasks.

Abstract

The Convolutional Neural Network has amazed us with its usage on several applications. Age range estimation using CNN is emerging due to its application in myriad of areas which makes it a state-of-the-art area for research and improve the estimation accuracy. A deep CNN model is used for identification of people's age range in our proposed work. At first, we extracted only face images from image dataset using MTCNN to remove unnecessary features other than face from the image. Secondly, we used random crop technique for data augmentation to improve the model performance. We have used the concept of transfer learning in our research. A pretrained face recognition model i.e VGG-Face is used to build our model for identification of age range whose performance is evaluated on Adience Benchmark for confirming the efficacy of our work. The performance in test set outperformed existing state-of-the-art by substantial margins.