Robust regression with deep CNNs for facial age estimation: An empirical study

Recent works have shown that deep Convolutional Neural Networks (CNNs) can be very effective for image-based age estimation. However, the proposed approaches significantly vary, and there are still some open problems. Almost all deep regression networks for age estimation have exploited the Mean Square Error loss only. These deep networks have not considered the influence of aberrant and outlier observations on the final model. In this letter, we introduce the use of robust loss functions in order to learn deep regression networks for age estimation. More precisely, we explore the use of two robust regression functions: (i) the ℓ1 norm error, and (ii) the adaptive loss function that retains the advantages of the ℓ1 and ℓ2 norms. Experimental results obtained on four public databases demonstrate that learning a deep CNN with robust losses can improve the age estimation.