Advancements in Age Estimation Using Facial Images

Estimating a person's age from their facial image is a growing area of interest. This task can be tough because obtaining a large set of images of the same person at different ages is difficult. Instead, the proposal here focuses on using available datasets that show different people at various ages. The goal is to find features that relate to age while reducing the influence of identity on the results.

#The Challenge of Age Estimation

Age estimation involves analyzing the visual features present in facial images. Different aspects of a person’s face, like wrinkles and skin texture, change with age. However, different people have different facial structures, making it challenging to single out age-related features. Most existing methods use large datasets, but gathering a series of images of the same person across their life is not easy.

Traditionally, age estimation methods can be divided into three categories: classification, regression, and ranking. Recently, new techniques like self-supervised learning have been introduced. However, many of these methods focus too much on identifying a person instead of their age, which can lead to inaccuracies.

#How It Works

The proposed method aims to improve age estimation by using a technique called Contrastive Learning. This technique compares images to emphasize age-related features while downplaying features linked to identity. To achieve this, the method uses sets of three images. One image acts as an anchor, one is similar in age but different in identity, and the third is different in both age and identity.

By looking at these images together, the system tries to reduce the influence of identity on age prediction. This means that it can focus on smaller details that denote age, rather than becoming biased by who the person is.

#Data Sets and Protocols

The method was tested on two publicly available datasets: MORPH II and FG-NET. The MORPH II dataset contains over 55,000 images from about 13,600 individuals, with ages ranging from 16 to 77 years. FG-NET has over 1,000 images of 82 individuals, with ages from newborns to 69 years old. These datasets have been used in various studies, making them ideal for comparing results.

#Implementation Steps

Before analysis, all images were aligned to ensure consistency. A model called ResNet-18 was used to extract features from these normalized images. During training, various image augmentations, like random flips and transformations, helped improve the model's ability to generalize.

The training was conducted using an optimizer called Adam, which adjusts learning rates during training. The model was assessed based on the Mean Absolute Error (MAE), which measures the difference between predicted ages and actual ages.

#Results and Comparisons

When evaluating the performance of the proposed model on the MORPH II dataset, it showed promising results, achieving a low MAE, meaning its age predictions were quite accurate. Compared to other methods that require large external datasets for training, this model performed well using only the available data.

The performance on the FG-NET dataset was also strong. The key takeaway is that the method worked well across different age groups without needing additional data, which is a significant advantage.

#Focusing on Features

To ensure the model was focusing on age-related features, researchers compared the variance of features extracted from faces of the same identity. A lower variance would indicate that the model relied heavily on identity features, which is not desirable for an age prediction task. The method demonstrated higher variance in extracted features compared to traditional methods, suggesting that it successfully emphasized age-related details.

#Visual Analysis

A Grad-CAM analysis was performed to visualize which parts of the face the model focused on during age estimation. This analysis showed that the proposed model targeted specific facial regions more related to age, such as the forehead for middle-aged individuals and areas around the mouth for younger subjects. This focus on particular features indicates that the model does not simply rely on generic facial structures but instead looks for age-specific signs.

#Evaluating Loss Functions

The research also explored how different loss functions impacted performance. By comparing various combinations of loss functions, it was evident that certain configurations worked better than others. The interaction between cosine similarity and Triplet Margin Loss was particularly effective, allowing the model to achieve better results on both small and large datasets.

Through rigorous testing, the study found that using a combination of both types of loss functions led to the best performance. In particular, models that included triplet margin loss showed improved accuracy, especially in smaller datasets.

#Conclusion

In summary, the method introduced for age estimation from facial images utilizes contrastive learning to effectively reduce the impact of identity on age prediction. By focusing on the relevant features associated with aging while minimizing the influence of identity-related traits, this approach has demonstrated strong performance on various datasets.

The research highlighted that by carefully selecting how to compare images and what features to emphasize, it is possible to make accurate age predictions without relying on extensive datasets of individual faces. This method shows promise for future applications in fields such as security, marketing, and healthcare where understanding age from images can provide valuable insights.