Age and gender classification from ear images

Abstract

In this paper, we present a detailed analysis on extracting soft biometric traits, age and gender, from ear images. Although there have been a few previous work on gender classification using ear images, to the best of our knowledge, this study is the first work on age classification from ear images. In the study, we have utilized both geometric features and appearance-based features for ear representation. The utilized geometric features are based on eight anthropometric landmarks and consist of 14 distance measurements and two area calculations. The appearance-based methods employ deep convolutional neural networks for representation and classification. The well-known convolutional neural network models, namely, AlexNet, VGG-16, GoogLeNet, and SqueezeNet have been adopted for the study. They have been fine-tuned on a large-scale ear dataset that has been built from the profile and close-to-profile face images in the Multi-PIE face dataset. This way, we have performed a domain adaptation. The updated models have been fine-tuned once more time on the small-scale target ear dataset, which contains only around 270 ear images for training. According to the experimental results, appearance-based methods have been found to be superior to the methods based on geometric features. We have achieved 94% accuracy for gender classification, whereas 52% accuracy has been obtained for age classification. These results indicate that ear images provide useful cues for age and gender classification, however, further work is required for age estimation.

Multimodal Age and Gender Classification Using Ear and Profile Face Images

“…According to the experiments on FERET dataset, the best result is obtained using ResNet-152 features with 5.50 mean absolute error (MAE). In [30], the authors present a study on age and gender classification from ear images. They employed both a geometric-based -distances between ear landmarks and area information-and an appearance-based representation -deep CNNs.…”

“…They employed both a geometric-based -distances between ear landmarks and area information-and an appearance-based representation -deep CNNs. The authors conducted their experiments on an internal dataset and found that appearancebased representation is more useful [30].…”

“…For age classification task, we have five different age groups based on the following age ranges: 18-28, 29-38, 39-48, 49-58, 59-68+. These classes have been selected according to the previous work about age classification from ear images [30]. According to the [30], the changes in the ear can be observable between these age groups.…”

“…In recent years, deep convolutional neural network (CNN) based approaches [17,22,30] have been commonly used for automatic age and gender classification. Generally, frontal or close to frontal face images have been used in these studies [17,18,10,34,22].…”

“…ages [3], and combination of them as well [33]. However, there is only one recent work [30] that performs age classification from ear images and one that performs age estimation from profile face images [3].…”